CN117217719B - Talent information recruitment data intelligent management method and system based on big data - Google Patents
Talent information recruitment data intelligent management method and system based on big data Download PDFInfo
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- 230000008520 organization Effects 0.000 claims abstract description 50
- 238000000034 method Methods 0.000 claims abstract description 29
- 230000004927 fusion Effects 0.000 claims description 92
- 238000013523 data management Methods 0.000 claims description 10
- 238000012502 risk assessment Methods 0.000 claims description 6
- 230000008569 process Effects 0.000 description 7
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Abstract
The invention discloses a talent information recruitment data intelligent management method and system based on big data, wherein the method comprises the following steps: confirming a plurality of associated organization nodes based on talent information uploaded by the client terminal; the plurality of associated organization nodes carry out multiparty authentication on the talent information, and the talent information is linked after the multiparty authentication is passed; confirming a training model based on the analysis request of the client terminal; confirming a plurality of training nodes based on the training model and the talent information after the uplink; each training node trains the training model by using local data to acquire training content; the training results are validated based on training content of the plurality of training nodes. The method solves the technical problems of scattered information resources, low sharing degree, single service content, insufficient personalized service and network information resources to be developed in talent information management in the prior art.
Description
Technical Field
The invention relates to the technical field of talent information recruitment data management, in particular to a talent information recruitment data intelligent management method and system based on big data.
Background
The information resources of the skill talents are the basis and effective grippers of the skill talent management work, and the massive skill talent information resources are stored, managed and utilized to comprehensively, accurately and dynamically master the skill talent stock and the skill talent structure information and clear the skill talent bottoms; can better realize the macroscopic management of the talents of the skills and exert the functions of the talents of the skills.
In the prior art, talent information recruitment data has the following problems in aspects of development, management and the like:
1. the sharing of information is easy to cause data repetition, human resource enterprises in the global scope at present are in intermediation and platform operation, and a certain competition relationship exists between human resource institutions, so that the sharing of information cannot be realized. Recruiters need to perform recruitment cooperation with different recruitment platforms to pay corresponding fees; meanwhile, job seekers also need to deliver resume on different platforms, which is easy to cause repeated data precipitation.
2. The job seeker information cannot be verified, the resume counterfeiting condition is serious, the human resource enterprises generally have no healthy cost early warning system, the authenticity of user data cannot be verified, and a large number of users are unreal, imperfect and the like. The time cost, labor cost, etc. generated during this period can result in significant losses to the relevant recruiter.
3. Personal information is unsafe and easy to leak, a human resource company is a completely centralized intermediary mechanism, personal information records cannot be completely transparent, and each recruitment platform in the global scope has leakage buying and selling user resume at present, so that serious loss is easily caused to users.
4. The recruitment cost is high, and as each recruitment platform and human resource company cannot realize complete information sharing, recruiters need to issue recruitment information on different platforms, so that the recruitment cost of the recruiters is greatly increased.
Disclosure of Invention
The invention aims to provide a talent information recruitment data intelligent management method and system based on big data, which solve the technical problems of scattered information resources, low sharing degree, single service content, insufficient personalized service and network information resources to be developed in the talent information management aspect in the prior art.
The invention provides a talent information recruitment data intelligent management method based on big data, which comprises the following steps:
confirming a plurality of associated organization nodes based on talent information uploaded by the client terminal;
the plurality of associated organization nodes carry out multiparty authentication on the talent information, and the talent information is linked after the multiparty authentication is passed;
confirming a training model based on the analysis request of the client terminal; confirming a plurality of training nodes based on the training model and the talent information after the uplink;
each training node trains the training model by using local data to acquire training content;
the training results are validated based on training content of the plurality of training nodes.
Further, performing multiparty authentication on the talent information, including:
acquiring learning data, working data and training data in talent information;
according to the learning data, the working data and the training data, a plurality of academic authentication requests, a plurality of working authentication requests and a plurality of training authentication requests are respectively obtained;
each academic authentication request, each work authentication request and each training authentication request are respectively sent to the corresponding associated organization nodes to acquire authentication signatures.
Further, obtaining the authentication signature includes:
each associated organization node receives an academic authentication request, a work authentication request or a training authentication request; confirming local data corresponding to the identity data based on the identity data in the academic authentication request, the work authentication request or the training authentication request;
confirming whether learning data in a corresponding academic authentication request, working data in a working authentication request or training data in a training authentication request are correct or not based on the local data;
if yes, generating an authentication signature; if not, generating an alarm signature.
Further, identifying a plurality of training nodes based on the training model and the talent information after the uplink includes:
acquiring an organization node list corresponding to the training model;
and acquiring a plurality of training nodes from a plurality of associated organization nodes associated with talent information according to the organization node list.
Further, before each training node trains the training model using the local data to obtain training content, the method further comprises:
confirming whether each training node has the training model or not;
if yes, obtaining queuing data;
if not, configuring the training model into a training node, and acquiring queuing data.
Further, the method further comprises the following steps:
confirming training end time based on queuing data and local data of each training node;
confirming the fusion time based on the training ending time of each training node;
confirming the publication time of the training result based on the training contents of the plurality of training nodes and the fusion time, and sending the publication time to the client terminal.
Further, confirming the fusion time based on the training end time of each training node includes:
acquiring the latest training ending time in the training ending time of a plurality of training nodes as the predicted fusion time;
acquiring a fusion schedule, and judging whether a fusion plan exists in the fusion schedule at the predicted fusion time;
if not, a fusion plan is newly built into a fusion plan table according to the predicted fusion time, and the predicted fusion time is used as the fusion time;
if yes, a fusion plan is newly built in the fusion plan table, and fusion time is obtained according to the fusion plan table.
The invention also provides a talent information recruitment data intelligent management system based on big data, which executes a talent information recruitment data intelligent management method based on big data, and the system comprises the following steps:
the device comprises a receiving module, an authentication module, a sending module and a fusion module;
the receiving module is used for receiving talent information uploaded by the client terminal and sending the talent information to the authentication module; the receiving module is also used for receiving the authentication signature of the associated organization node and sending the authentication signature to the authentication module; the receiving module is also used for receiving training contents uploaded by the training nodes and sending the training contents to the fusion module;
the authentication module is used for acquiring a plurality of associated organization nodes according to talent information, generating a plurality of academic authentication requests, a plurality of work authentication requests and a plurality of training authentication requests according to the talent information, and respectively transmitting the requests to the corresponding associated organization nodes through the transmitting module; the authentication module also confirms whether the authentication signature is legal;
the fusion module is used for fusing training contents of a plurality of training nodes to obtain training results;
the organization node is used for responding to the academic authentication request, the work authentication request or the training authentication request and generating an authentication signature, and the organization node is used as a training node to acquire training content.
Further, the system also comprises a storage module, wherein the storage module stores a plurality of training models.
Further, the plurality of training models includes:
an innovation analysis model for analyzing innovation ability of talents;
the expertise analysis model is used for analyzing the expertise of talents to the industry;
and the risk analysis model is used for evaluating recruitment risks of talents.
Compared with the prior art, the invention has the beneficial effects that:
in the embodiment, the talent information is subjected to multiparty authentication, and the authenticity of the talent information uploaded by the client terminal can be ensured after the multiparty authentication is passed, so that a personnel unit can conveniently screen according to the authentic talent information; the corresponding training model is confirmed by acquiring the analysis request of the client terminal, and the corresponding plurality of organization nodes are simultaneously confirmed to serve as the training nodes, and the training content is acquired by training the training model by acquiring the local data in each training node, so that the training content can be acquired on the premise of ensuring the safety of the local data of each training node, and the data leakage is avoided; the auxiliary decision is made by fusing a plurality of training contents and obtaining a training result. The system can provide valuable talent information service for demand parties such as scientific research institutions, universities, enterprises, personal scientific research personnel and the like, improve the overall level of talent management, and provide data support for researches in the directions of talent back adjustment analysis, staff education analysis, professional development analysis, wind control analysis and the like. The method solves the technical problems of scattered information resources, low sharing degree, single service content, insufficient personalized service and network information resources to be developed in talent information management in the prior art.
Drawings
FIG. 1 is a flowchart illustrating steps of a method according to an embodiment of the present invention.
Fig. 2 is a schematic structural diagram of another embodiment of the present invention.
Detailed Description
As shown in fig. 1, a talent information recruitment data intelligent management method based on big data, the method includes:
confirming a plurality of associated organization nodes based on talent information uploaded by the client terminal;
the plurality of associated organization nodes carry out multiparty authentication on the talent information, and the talent information is linked after the multiparty authentication is passed;
confirming a training model based on the analysis request of the client terminal; confirming a plurality of training nodes based on the training model and the talent information after the uplink;
each training node trains the training model by using local data to acquire training content;
the training results are validated based on training content of the plurality of training nodes.
The implementation process of the embodiment comprises the following steps:
it should be noted that, the organization nodes are all nodes of each unit or individual in the blockchain; the data transfer in this embodiment is performed by the blockchain technique.
In this embodiment, performing multiparty authentication on the talent information includes:
acquiring learning data, working data and training data in talent information; according to the learning data, the working data and the training data, a plurality of academic authentication requests, a plurality of working authentication requests and a plurality of training authentication requests are respectively obtained; the learning data comprises a learning experience, a learning score and a certificate in the learning process; the work data includes credentials acquired during a work experience, a work; the training data comprises training experience and training certificates obtained by training. The organization node comprises an authenticated unit or organization; the organization nodes associated with talent information include work units of the talents, just-read schools, units for organization training, units for issuing certificates and the like;
sending each academic authentication request, each work authentication request and each training authentication request to a corresponding associated organization node to obtain an authentication signature, respectively, comprising:
each associated organization node receives an academic authentication request, a work authentication request or a training authentication request; confirming local data corresponding to the identity data based on the identity data in the academic authentication request, the work authentication request or the training authentication request;
confirming whether learning data in a corresponding academic authentication request, working data in a working authentication request or training data in a training authentication request are correct or not based on the local data;
if yes, generating an authentication signature; if not, generating an alarm signature. When the alarm signature is generated, the fact that the data corresponding to the alarm signature in the talent information does not pass at the moment is indicated, and the suspected person is suspected to be fake.
In this embodiment, the identifying a plurality of training nodes based on the training model and the talent information after the uplink includes:
acquiring an organization node list corresponding to the training model;
and acquiring a plurality of training nodes from a plurality of associated organization nodes associated with talent information according to the organization node list.
The training nodes are the organization nodes, a plurality of training models are stored in the training nodes, and training is carried out on the training models through local data to obtain training contents.
In this embodiment, the training content includes results obtained by the talents through multiple training models on the local data of each training node; for example, in some embodiments, the training model includes an innovation analysis model for analyzing talents 'innovation ability, a specialty analysis model for analyzing talents' expertise for the industry, and a risk analysis model for evaluating talent recruitment risk;
the training content can provide various corresponding capabilities of talents in each training node for analysis in recruitment;
the training results comprise results obtained by comprehensively analyzing training contents of a plurality of training nodes; the talent information recruitment management system can provide the current overall comprehensive capacity of talents so as to facilitate talent information recruitment management.
It should be noted that, in this embodiment, both units and individuals may upload talent information through the client terminal and send an analysis request.
In the embodiment, the talent information is subjected to multiparty authentication, and the authenticity of the talent information uploaded by the client terminal can be ensured after the multiparty authentication is passed, so that a personnel unit can conveniently screen according to the authentic talent information; the corresponding training model is confirmed by acquiring the analysis request of the client terminal, and the corresponding plurality of organization nodes are simultaneously confirmed to serve as the training nodes, and the training content is acquired by training the training model by acquiring the local data in each training node, so that the training content can be acquired on the premise of ensuring the safety of the local data of each training node, and the data leakage is avoided; the auxiliary decision is made by fusing a plurality of training contents and obtaining a training result. The method solves the technical problems of scattered information resources, low sharing degree, single service content, insufficient personalized service and network information resources to be developed in talent information management in the prior art.
In another embodiment of the present invention, before each training node trains the training model using the local data to obtain training content, the method further includes:
confirming whether each training node has the training model or not;
if yes, obtaining queuing data;
if not, configuring the training model into a training node, and acquiring queuing data.
The specific implementation process of the embodiment comprises the following steps:
in the present embodiment of the present invention,
confirming training end time based on queuing data and local data of each training node;
confirming the fusion time based on the training ending time of each training node; comprising the following steps:
acquiring the latest training ending time in the training ending time of a plurality of training nodes as the predicted fusion time;
acquiring a fusion schedule, and judging whether a fusion plan exists in the fusion schedule at the predicted fusion time;
if not, a fusion plan is newly built into a fusion plan table according to the predicted fusion time, and the predicted fusion time is used as the fusion time;
if yes, a fusion plan is newly built in the fusion plan table, and fusion time is obtained according to the fusion plan table.
Confirming the publication time of the training result based on the training contents of the plurality of training nodes and the fusion time, and sending the publication time to the client terminal.
In this embodiment, the predicted fusion time is obtained by obtaining queuing data corresponding to each training node, and the latest queuing time in all queuing data, then the real fusion time is determined according to the fusion plan, and the final publishing time is determined according to the training content, and the publishing time is sent to the client terminal, so that the user can conveniently schedule other works before the publishing time arrives. Avoiding long waiting time of the user.
The fusion plan in this embodiment includes a process of confirming training results according to training contents of a plurality of training nodes at corresponding fusion times; the fusion time corresponding to each fusion plan is influenced by two factors, namely the predicted fusion time, namely the latest queuing time in queuing data; and secondly, fusion time corresponding to other fusion plans in the fusion plan table.
When another fusion plan exists in the fusion plan table and needs to be executed, judging that the fusion plan exists in the fusion plan table and the fusion time of the other fusion plans in the fusion plan table, queuing according to the fusion time of the other fusion plans in the fusion plan table, and selecting a time nearby to newly establish the fusion plan compared with the predicted fusion time, wherein the selected time is the fusion time of the current fusion plan.
And when other fusion plans do not exist in the fusion schedule at the predicted fusion time and need to be executed, judging that the fusion plan does not exist at the predicted fusion time in the fusion schedule. At this time, a fusion plan is directly created according to the predicted fusion time. As shown in fig. 2, in another embodiment of the present invention, an intelligent talent information recruitment data management system based on big data, performs an intelligent talent information recruitment data management method based on big data, the system includes:
the device comprises a receiving module, an authentication module, a sending module and a fusion module;
the receiving module is used for receiving talent information uploaded by the client terminal and sending the talent information to the authentication module; the receiving module is also used for receiving the authentication signature of the associated organization node and sending the authentication signature to the authentication module; the receiving module is also used for receiving training contents uploaded by the training nodes and sending the training contents to the fusion module;
the authentication module is used for acquiring a plurality of associated organization nodes according to talent information, generating a plurality of academic authentication requests, a plurality of work authentication requests and a plurality of training authentication requests according to the talent information, and respectively transmitting the requests to the corresponding associated organization nodes through the transmitting module; the authentication module also confirms whether the authentication signature is legal;
the fusion module is used for fusing training contents of a plurality of training nodes to obtain training results;
the organization node is used for responding to the academic authentication request, the work authentication request or the training authentication request and generating an authentication signature, and the organization node is used as a training node to acquire training content.
The implementation process of the embodiment comprises the following steps:
in this embodiment, the system further includes a storage module, where the storage module stores a plurality of training models. Each training model corresponds to a capability analysis, for example, a training model can analyze the learning capability of a user, and the corresponding learning capability in the organization nodes can be used as training nodes and comprise: primary school, junior middle school, college middle school, and university school; training the training model by the local data of talents at a primary school, a junior middle school, a college school and a university school respectively; the method and the device avoid the transmission of local data and prevent leakage in the transmission process of the local data.
In another embodiment of the present invention, the plurality of training models includes:
an innovation analysis model for analyzing innovation ability of talents;
the expertise analysis model is used for analyzing the expertise of talents to the industry;
and the risk analysis model is used for evaluating recruitment risks of talents.
The implementation process of the embodiment comprises the following steps:
in the embodiment, the innovation analysis model, the expertise analysis model and the risk analysis model are all obtained by training a convolutional neural network model through a check data set, and are verified by a verification data set; the expertise analysis model is used for analyzing the expertise of talents in the industry, and personnel units and individuals can acquire the expertise of the talents through the training result of the expertise analysis model; the higher the expertise, the more known the industry; the innovation analysis model is used for analyzing innovation capacity of talents, personnel units and individuals can analyze the innovation capacity of the talents through the innovation analysis model, for individuals, the evaluation of the innovation capacity of the individuals can help the individuals to select the talents when the talents are employment, and when the innovation capacity of the individuals is general, industries and positions with higher requirements on the innovation capacity are avoided as much as possible; when the innovation capability of the self-body is stronger, the industries and posts which have general requirements on the innovation capability but higher requirements on the capability in other aspects can be avoided, so that individuals can find out the work suitable for themselves; for personnel units, the requirements of industry and posts on innovation capability can be met, personnel with innovation capability meeting the requirements can be quickly matched when personnel are selected, further screening is facilitated, the phenomenon of personnel waste caused by non-matching of posts and capability is avoided, and recruitment cost of the personnel units is reduced.
The risk analysis model is used for evaluating recruitment risk of talents, and in this embodiment, the recruitment risk of talents includes: physical quality risks and occupational ethics risks;
wherein the physical fitness risk comprises: hypertension, psychological diseases, and the like; certain industries or posts have certain specificity, and the requirements on physical quality of employment staff are high;
for example, the demands of talents on high-rise work include cardiovascular and cerebrovascular diseases such as hypertension and heart diseases, and recruitment risks for talents older and having cardiovascular and cerebrovascular diseases for the unit of people are greater;
for another example, the operation post of the high-risk major equipment has higher requirements on the psychological health condition of the on-duty personnel, and when a technical talent exists recently or is currently in the process of the onset of psychological diseases, the physical quality risk is higher, and the recruitment risk is higher;
occupational moral risks include: records affecting professional morals exist on previous similar posts, for example; the method comprises the steps that a record of stealing public money exists on a previous accounting post of an accounting talent, and the occupational moral risk of the accounting talent is higher; talents with a high risk of occupational morals for a human entity need careful handling during recruitment.
The above embodiments are only for illustrating the technical method of the present invention and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted without departing from the spirit and scope of the technical method of the present invention.
Claims (7)
1. The intelligent talent information recruitment data management method based on big data is characterized by comprising the following steps:
confirming a plurality of associated organization nodes based on talent information uploaded by the client terminal; the talent information comprises learning data, working data and training data; the organization nodes associated with the talent information comprise a talent work unit, a read-just school, an organization training unit and a certificate issuing unit;
the plurality of associated organization nodes carry out multiparty authentication on the talent information, and the talent information is linked after the multiparty authentication is passed;
confirming a training model based on the analysis request of the client terminal; confirming the plurality of training nodes based on the training model and the talent information after the uplink, comprising: acquiring an organization node list corresponding to the training model; according to the organization node list, a plurality of training nodes are obtained from a plurality of associated organization nodes associated with talent information; the training nodes store a plurality of training models;
each training node trains the training model by using local data to acquire training content; the training content provides various corresponding capabilities of talents in each training node for analysis in recruitment;
confirming training results based on training contents of a plurality of training nodes, wherein the training results provide current overall comprehensive capacity of talents;
before each training node trains the training model by using the local data to acquire training content, the method further comprises the following steps:
confirming whether each training node has the training model or not;
if yes, obtaining queuing data;
if not, configuring the training model into a training node, and acquiring queuing data.
2. The intelligent talent information recruitment data management method based on big data as claimed in claim 1, wherein: performing multiparty authentication on the talent information, including:
acquiring learning data, working data and training data in talent information;
according to the learning data, the working data and the training data, a plurality of academic authentication requests, a plurality of working authentication requests and a plurality of training authentication requests are respectively obtained;
each academic authentication request, each work authentication request and each training authentication request are respectively sent to the corresponding associated organization nodes to acquire authentication signatures.
3. The intelligent talent information recruitment data management method based on big data as claimed in claim 2, wherein: acquiring an authentication signature, comprising:
each associated organization node receives an academic authentication request, a work authentication request or a training authentication request; confirming local data corresponding to the identity data based on the identity data in the academic authentication request, the work authentication request or the training authentication request;
confirming whether learning data in a corresponding academic authentication request, working data in a working authentication request or training data in a training authentication request are correct or not based on the local data;
if yes, generating an authentication signature; if not, generating an alarm signature.
4. The intelligent talent information recruitment data management method based on big data as claimed in claim 1, wherein: further comprises:
confirming training end time based on queuing data and local data of each training node;
confirming the fusion time based on the training ending time of each training node;
confirming the publication time of the training result based on the training contents of the plurality of training nodes and the fusion time, and sending the publication time to the client terminal.
5. The intelligent talent information recruitment data management method based on big data according to claim 4, wherein: confirming the fusion time based on the training end time of each training node, comprising:
acquiring the latest training ending time in the training ending time of a plurality of training nodes as the predicted fusion time;
acquiring a fusion schedule, and judging whether a fusion plan exists in the fusion schedule at the predicted fusion time;
if not, a fusion plan is newly built into a fusion plan table according to the predicted fusion time, and the predicted fusion time is used as the fusion time;
if yes, a fusion plan is newly built in the fusion plan table, and fusion time is obtained according to the fusion plan table.
6. The intelligent talent information recruitment data management system based on big data is characterized in that: an intelligent management method for implementing the talent information recruitment data based on big data according to any one of claims 1-5, said system comprising:
the device comprises a receiving module, an authentication module, a sending module, a storage module and a fusion module;
the receiving module is used for receiving talent information uploaded by the client terminal and sending the talent information to the authentication module; the talent information comprises learning data, working data and training data; the receiving module is also used for receiving the authentication signature of the associated organization node and sending the authentication signature to the authentication module; the receiving module is also used for receiving training contents uploaded by the training nodes and sending the training contents to the fusion module;
the authentication module is used for acquiring a plurality of associated organization nodes according to talent information, wherein the organization nodes associated with the talent information comprise a talent work unit, a read school, an organization training unit and a certificate issuing unit; the authentication module generates a plurality of academic authentication requests, a plurality of work authentication requests and a plurality of training authentication requests according to the talent information and respectively sends the requests to corresponding associated organization nodes through the sending module; the authentication module also confirms whether the authentication signature is legal;
the fusion module is used for fusing training contents of a plurality of training nodes to obtain training results, and the training results provide current overall comprehensive capacity of talents;
the organization node is used for responding to the academic authentication request, the work authentication request or the training authentication request and generating an authentication signature, and is also used as a training node to acquire training contents, wherein the training contents provide various corresponding capabilities of talents for recruitment;
and the storage module is used for storing a plurality of training models.
7. The intelligent talent information recruitment data management system based on big data of claim 6, wherein: the number of training models includes:
an innovation analysis model for analyzing innovation ability of talents;
the expertise analysis model is used for analyzing the expertise of talents to the industry;
and the risk analysis model is used for evaluating recruitment risks of talents.
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