Talent information processing method and system based on occupational attainment and big data analysis

Abstract

The invention discloses a talent information processing method and system based on occupational attainment and big data analysis, and relates to the technical field of talent information processing.The method comprises the steps that talent information from multiple channels is integrated through a multi-source data fusion algorithm, and a talent information set is obtained; performing feature extraction on the information in the talent information set to identify unstructured features related to the occupational quality of the talents; determining a multi-level occupational attainment evaluation and prediction model based on the unstructured features related to the occupational attainment; determining a talent matching algorithm based on a graph theory according to an output result of the multi-level occupational attainment evaluation and prediction model; wherein the talent matching algorithm is used for outputting a matching graph between talents and positions; and determining a talent configuration plan for the target object according to a matching graph between the talents and the positions output by the talent matching algorithm.

Description

Technical Field

[0001] The present application relates to the technical field of talent information processing, and more specifically, to a talent information processing method and system based on professional literacy and big data analysis. Background Art

[0002] With the advent of economic globalization and the information age, enterprises are facing more and more competition and challenges, and the importance of talent information processing is becoming increasingly prominent. Talent information processing is not only related to whether enterprises can efficiently attract and retain key talents, but also directly affects the operational efficiency and strategic decision-making of enterprises.

[0003] Effective talent information processing helps identify and prevent potential human resource risks, such as employee turnover and skill gaps, thereby reducing the negative impact on the business. Talent information processing can also reveal new work models and talent trends, providing data support for corporate innovation and business development. Existing talent information processing methods often rely on manual screening and matching, which is inefficient and easily affected by subjective factors.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] The embodiments of the present application provide a talent information processing method and system based on professional literacy and big data analysis to solve the above-mentioned technical problems.

[0006] The present application provides a talent information processing method based on professional literacy and big data analysis, including: integrating talent information from multiple channels through a multi-source data fusion algorithm to obtain a talent information set; Extracting features from the information in the talent information set to identify unstructured features related to the professional qualities of the talent; Based on the unstructured characteristics related to professionalism, a multi-level professionalism assessment and prediction model is determined; Determine a talent matching algorithm based on graph theory according to the output results of the multi-level professional quality assessment and prediction model; wherein the talent matching algorithm is used to output a matching graph between talents and positions; According to the matching graph between talents and positions output by the talent matching algorithm, a talent allocation plan is determined for the target object.

[0007] Furthermore, the multi-level professional quality assessment and prediction model is determined based on the unstructured features related to professional quality, and is configured as follows: Based on the unstructured characteristics related to professional accomplishment, the individual comprehensive ability assessment model, the professional adaptability and development potential sub-model, and the social network and influence assessment sub-model are determined; The multi-level professional quality assessment and prediction model is obtained by integrating the individual comprehensive ability assessment model, the occupational adaptability and development potential sub-model and the social network and influence assessment sub-model.

[0008] Further, the plurality of channels include social media, online resumes, and corporate data; The target objects are colleges and universities, enterprises or institutions.

[0009] The present application provides a talent information processing system based on professional literacy and big data analysis, including: a talent information integration module, which is used to integrate talent information from multiple channels through a multi-source data fusion algorithm to obtain a talent information set; A professional quality information extraction module is used to extract features from the information in the talent information set to identify unstructured features related to the professional quality of the talent; The professional quality assessment and prediction module is used to determine a multi-level professional quality assessment and prediction model based on unstructured features related to professional quality; A talent matching algorithm building module is used to determine a talent matching algorithm based on graph theory according to the output results of the multi-level professional quality assessment and prediction model; wherein the talent matching algorithm is used to output a matching graph between talents and positions; The talent allocation plan determination unit is used to determine a talent allocation plan for a target object according to a matching graph between talents and positions output by the talent matching algorithm.

[0010] Furthermore, the professional quality assessment and prediction model includes three levels of sub-models, namely, individual comprehensive ability assessment model, professional adaptability and development potential sub-model, and social network and influence assessment sub-model; the professional quality assessment and prediction module determines a multi-level professional quality assessment and prediction model based on the unstructured features related to professional quality, and is configured as follows: Based on the unstructured characteristics related to professional accomplishment, the individual comprehensive ability assessment model, the professional adaptability and development potential sub-model, and the social network and influence assessment sub-model are determined; The multi-level professional quality assessment and prediction model is obtained by integrating the individual comprehensive ability assessment model, the occupational adaptability and development potential sub-model and the social network and influence assessment sub-model.

[0011] Based on the embodiment provided by the present application, talent information from multiple channels is integrated through a multi-source data fusion algorithm to obtain a talent information set; feature extraction is performed on the information in the talent information set to identify unstructured features related to the professional qualities of the talent; based on the unstructured features related to professional qualities, a multi-level professional quality assessment and prediction model is determined; according to the output results of the multi-level professional quality assessment and prediction model, a talent matching algorithm based on graph theory is determined; wherein the talent matching algorithm is used to output a matching graph between talents and positions; according to the matching graph between talents and positions output by the talent matching algorithm, a talent allocation plan is determined for the target object. Thus, the processing of talent information based on professional qualities and big data analysis is realized automatically, and the efficiency and accuracy of processing talent information are improved. Specifically, it has the following beneficial effects: through the multi-source data fusion algorithm, talent information from different channels can be integrated to form a comprehensive talent information set. This method helps to capture the full range of characteristics of talents and improves the richness and accuracy of talent information; by extracting features from talent information sets and identifying unstructured features related to professionalism, the evaluation model can have a deeper understanding of the potential abilities and qualities of talents, thereby improving the accuracy and reliability of the evaluation; a multi-level professionalism evaluation and prediction model is established based on unstructured features, which can not only evaluate an individual's current professionalism, but also predict their future development potential, providing a longer-term perspective for talent management; using a talent matching algorithm based on graph theory, the best match between talent and position can be determined based on the output results of the multi-level evaluation and prediction model. Matching; This method helps to achieve accurate matching of talents and positions, improve recruitment efficiency and employee satisfaction; the matching graph between talents and positions output by the talent matching algorithm provides decision makers with an intuitive way to understand and analyze talent allocation plans, making the talent allocation process more transparent and easy to manage; based on the matching graph, a talent allocation plan can be determined for the target object, which helps companies to more effectively utilize talent resources, improve organizational effectiveness, and provide talents with positions that are more suitable for their career development paths; through multi-level evaluation, it helps companies better understand the long-term development potential of employees, thereby designing more effective career development paths and retention strategies, and enhancing employee loyalty and satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The drawings described herein are used to provide a further understanding of the embodiments of the present invention and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1 is a flowchart of an optional talent information processing method based on professional literacy and big data analysis according to an embodiment of the present application; Figure 2The present invention is a structural diagram of an optional talent information processing system based on professional quality and big data analysis according to an embodiment of the present application.

[0013] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0014] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0015] Alternatively, if Figure 1 As shown, the present application provides a talent information processing method based on professional literacy and big data analysis, including: S101, integrating talent information from multiple channels through a multi-source data fusion algorithm to obtain a talent information set; S102, extracting features from the information in the talent information set to identify unstructured features related to the professional qualities of the talent; S103, determine a multi-level professional literacy assessment and prediction model based on unstructured features related to professional literacy; S104, determining a talent matching algorithm based on graph theory according to the output results of the multi-level professional quality assessment and prediction model; wherein the talent matching algorithm is used to output a matching graph between talents and positions; S105, determining a talent allocation plan for the target object according to the matching graph between talents and positions output by the talent matching algorithm.

[0016] Based on the embodiment provided by the present application, talent information from multiple channels is integrated through a multi-source data fusion algorithm to obtain a talent information set; feature extraction is performed on the information in the talent information set to identify unstructured features related to the professional qualities of the talent; based on the unstructured features related to professional qualities, a multi-level professional quality assessment and prediction model is determined; according to the output results of the multi-level professional quality assessment and prediction model, a talent matching algorithm based on graph theory is determined; wherein the talent matching algorithm is used to output a matching graph between talents and positions; according to the matching graph between talents and positions output by the talent matching algorithm, a talent allocation plan is determined for the target object. Thus, the processing of talent information based on professional qualities and big data analysis is realized automatically, and the efficiency and accuracy of processing talent information are improved. Specifically, it has the following beneficial effects: through the multi-source data fusion algorithm, talent information from different channels can be integrated to form a comprehensive talent information set. This method helps to capture the full range of characteristics of talents and improves the richness and accuracy of talent information; by extracting features from talent information sets and identifying unstructured features related to professionalism, the evaluation model can have a deeper understanding of the potential abilities and qualities of talents, thereby improving the accuracy and reliability of the evaluation; a multi-level professionalism evaluation and prediction model is established based on unstructured features, which can not only evaluate an individual's current professionalism, but also predict their future development potential, providing a longer-term perspective for talent management; using a talent matching algorithm based on graph theory, the best match between talent and position can be determined based on the output results of the multi-level evaluation and prediction model. Matching; This method helps to achieve accurate matching of talents and positions, improve recruitment efficiency and employee satisfaction; the matching graph between talents and positions output by the talent matching algorithm provides decision makers with an intuitive way to understand and analyze talent allocation plans, making the talent allocation process more transparent and easy to manage; based on the matching graph, a talent allocation plan can be determined for the target object, which helps companies to more effectively utilize talent resources, improve organizational effectiveness, and provide talents with positions that are more suitable for their career development paths; through multi-level evaluation, it helps companies better understand the long-term development potential of employees, thereby designing more effective career development paths and retention strategies, and enhancing employee loyalty and satisfaction.

[0017] Furthermore, based on the unstructured features related to professionalism, a multi-level professionalism assessment and prediction model is determined, which is configured as follows: Based on the unstructured characteristics related to professionalism, the individual comprehensive ability assessment model, the professional adaptability and development potential sub-model, and the social network and influence assessment sub-model were determined; By integrating the individual comprehensive ability assessment model, the career adaptability and development potential sub-model, and the social network and influence assessment sub-model, a multi-level professional quality assessment and prediction model is obtained.

[0018] Further, multiple channels include social media, online resumes, and corporate data; The target objects are colleges and universities, enterprises or institutions.

[0019] Alternatively, if Figure 2 As shown, the present application provides a talent information processing system based on professional literacy and big data analysis, including: The talent information integration module 201 is used to integrate talent information from multiple channels through a multi-source data fusion algorithm to obtain a talent information set; The professional quality information extraction module 202 is used to extract features from the information in the talent information set to identify unstructured features related to the professional quality of the talent; A professional quality assessment and prediction module 203 is used to determine a multi-level professional quality assessment and prediction model based on unstructured features related to professional quality; The talent matching algorithm building module 204 is used to determine a talent matching algorithm based on graph theory according to the output results of the multi-level professional quality assessment and prediction model; wherein the talent matching algorithm is used to output a matching graph between talents and positions; The talent allocation plan determination unit 205 is used to determine a talent allocation plan for the target object according to the matching graph between talents and positions output by the talent matching algorithm.

[0020] Furthermore, the professional quality assessment and prediction model includes three levels of sub-models, namely, individual comprehensive ability assessment model, professional adaptability and development potential sub-model, and social network and influence assessment sub-model; the professional quality assessment and prediction module determines a multi-level professional quality assessment and prediction model based on the unstructured features related to professional quality, and is configured as follows: Based on the unstructured characteristics related to professionalism, the individual comprehensive ability assessment model, the professional adaptability and development potential sub-model, and the social network and influence assessment sub-model were determined; By integrating the individual comprehensive ability assessment model, the career adaptability and development potential sub-model, and the social network and influence assessment sub-model, a multi-level professional quality assessment and prediction model is obtained.

[0021] Furthermore, based on the unstructured characteristics related to professionalism, the individual comprehensive ability assessment model, the professional adaptability and development potential sub-model, and the social network and influence assessment sub-model are determined and configured as follows: Based on unstructured characteristics related to professionalism Train to obtain individual comprehensive ability evaluation model ;in, ; It is a function that combines machine learning and psychometrics. The output of the individual comprehensive ability assessment model is the score of the individual's ability in multiple dimensions. The abilities in multiple dimensions include cognitive ability, emotional intelligence, and leadership. Based on unstructured characteristics related to professionalism As well as the output of the individual comprehensive ability assessment model, time series analysis and reinforcement learning algorithms are used to simulate the individual's performance in the virtual career path to assess the individual's career adaptability and development potential in different career environments; based on the individual's adaptability and development potential in different career environments, the career adaptability and development potential sub-models are obtained. ; in, ; It is a function that combines time series analysis and reinforcement learning algorithms; the career adaptability and development potential sub-model is used to predict the individual's future growth trajectory and career development potential; Based on unstructured characteristics related to professionalism As well as the future growth trajectory and career development potential of individuals predicted by the career adaptability and development potential sub-model, the social network analysis algorithm and natural language processing algorithm are used to analyze the interaction patterns of individuals in social media and professional forums to determine the career adaptability and development potential sub-model ; in, ; It is a function that combines social network analysis algorithms and natural language processing algorithms. The career adaptability and development potential sub-model is used to evaluate the influence and social ability of individuals in professional networks to quantify the individual's social influence and network centrality. Based on the influence and social ability of individuals in professional networks obtained by the career adaptability and development potential sub-model, the career adaptability and development potential sub-model is updated to obtain the updated career adaptability and development potential sub-model. ;

[0022] in, is an updating function, which is used to update the career adaptability and development potential sub-model according to the individual’s influence and social ability in the professional network.

[0023] Furthermore, according to the output results of the multi-level professional quality assessment and prediction model, a talent matching algorithm based on graph theory is determined and configured as follows: Construct an initial graph model; the initial graph model includes talent nodes and position nodes; the node characteristics of each talent node include the output of the individual comprehensive ability assessment model, the career adaptability and development potential sub-model, and the social network and influence assessment sub-model; the node characteristics of each position node include the position description, required skills, and social influence; The word frequency-inverse document frequency algorithm and the word embedding algorithm are used together to calculate the first similarity between the talent node and the position node; By analyzing the relationship between talent nodes and the correlation between position nodes, the second similarity of the edges between talent nodes and the third similarity of the edges between position nodes are analyzed; Calculate the association matrix in graph matching according to the first similarity, the second similarity and the third similarity; Combining the decomposition graph matching algorithm and the probabilistic graph matching algorithm, a talent matching algorithm is constructed based on the association matrix in graph matching; The output results of multi-level professional quality assessment and prediction models are incorporated into the talent matching algorithm as weighted factors.

[0024] Furthermore, based on the following formula, the association matrix in the graph matching is calculated according to the first similarity, the second similarity and the third similarity:

[0025] in, is an element of the association matrix in graph matching, representing a talent node With position node The comprehensive similarity between It is a talent node With position node The first similarity between It is a talent node Talent Node The second similarity between Is a position node With position node The third similarity between , , Talent Node The output of the individual comprehensive ability assessment model, the career adaptability and development potential sub-model, and the social network and influence assessment sub-model; , , Position nodes feature vectors of job descriptions, required skills, and social influence; , , are all weight coefficients.

[0026] Furthermore, the talent node is calculated based on the following formula With position node The first similarity between :

[0027] in, It is the similarity calculated based on the word frequency-inverse document frequency algorithm; is the similarity calculated by the word embedding algorithm; , Talent Node The output of the individual comprehensive ability assessment model, career adaptability and development potential sub-model; , Position nodes The job description and the feature vector of required skills; , , are all weight coefficients.

[0028] Furthermore, the multiple channels include social media, online resumes, and corporate data; the target objects are colleges and universities, companies, or institutions.

[0029] It should be noted that in the present application, the embodiments implemented on the talent information processing system side based on professional literacy and big data analysis can be cross-referenced with the embodiments implemented on the talent information processing method side based on professional literacy and big data analysis, and this application will not go into details one by one.

[0030] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A talent information processing method based on professional literacy and big data analysis, characterized in that: include: Integrate talent information from multiple channels through multi-source data fusion algorithm to obtain talent information collection; Extracting features from the information in the talent information set to identify unstructured features related to the professional qualities of the talent; Based on the unstructured characteristics related to professionalism, a multi-level professionalism assessment and prediction model is determined; Determine a talent matching algorithm based on graph theory according to the output results of the multi-level professional quality assessment and prediction model; wherein the talent matching algorithm is used to output a matching graph between talents and positions; According to the matching graph between talents and positions output by the talent matching algorithm, a talent allocation plan is determined for the target object.

2. The talent information processing method based on professional quality and big data analysis according to claim 1 is characterized in that: The multi-level professional quality assessment and prediction model is determined based on the unstructured features related to professional quality and is configured as follows: Based on the unstructured characteristics related to professional accomplishment, the individual comprehensive ability assessment model, the professional adaptability and development potential sub-model, and the social network and influence assessment sub-model are determined; The multi-level professional quality assessment and prediction model is obtained by integrating the individual comprehensive ability assessment model, the occupational adaptability and development potential sub-model and the social network and influence assessment sub-model.

3. The talent information processing method based on professional quality and big data analysis according to claim 1 is characterized in that: The multiple channels include social media, online resumes, and corporate data; The target objects are colleges and universities, enterprises or institutions.

4. A talent information processing system based on professionalism and big data analysis, the system implements the talent information processing method based on professionalism and big data analysis as claimed in claim 1, characterized in that: include: The talent information integration module is used to integrate talent information from multiple channels through a multi-source data fusion algorithm to obtain a talent information set; A professional quality information extraction module is used to extract features from the information in the talent information set to identify unstructured features related to the professional quality of the talent; The professional quality assessment and prediction module is used to determine a multi-level professional quality assessment and prediction model based on unstructured features related to professional quality; A talent matching algorithm building module is used to determine a talent matching algorithm based on graph theory according to the output results of the multi-level professional quality assessment and prediction model; wherein the talent matching algorithm is used to output a matching graph between talents and positions; The talent allocation plan determination unit is used to determine a talent allocation plan for a target object according to a matching graph between talents and positions output by the talent matching algorithm.

5. The talent information processing system based on professional quality and big data analysis according to claim 4 is characterized in that: The professional quality assessment and prediction model includes three levels of sub-models, namely, individual comprehensive ability assessment model, professional adaptability and development potential sub-model, and social network and influence assessment sub-model; the professional quality assessment and prediction module determines a multi-level professional quality assessment and prediction model based on the unstructured features related to professional quality, and is configured as follows: Based on the unstructured characteristics related to professional accomplishment, the individual comprehensive ability assessment model, the professional adaptability and development potential sub-model, and the social network and influence assessment sub-model are determined; The multi-level professional quality assessment and prediction model is obtained by integrating the individual comprehensive ability assessment model, the occupational adaptability and development potential sub-model and the social network and influence assessment sub-model.

6. The talent information processing system based on professional quality and big data analysis according to claim 5 is characterized in that: The determination of the individual comprehensive ability assessment model, the occupational adaptability and development potential sub-model, and the social network and influence assessment sub-model based on the unstructured features related to professional accomplishment is configured as follows: Based on unstructured characteristics related to professionalism The individual comprehensive ability evaluation model is obtained by training ; The output of the individual comprehensive ability assessment model is a score of the individual's ability in multiple dimensions; the abilities in multiple dimensions include cognitive ability, emotional intelligence and leadership.

7. The talent information processing system based on professional quality and big data analysis according to claim 6 is characterized in that: The determination of the individual comprehensive ability assessment model, the occupational adaptability and development potential sub-model, and the social network and influence assessment sub-model based on the unstructured features related to occupational literacy is configured as follows: As well as the output of the individual comprehensive ability assessment model, time series analysis and reinforcement learning algorithms are used to simulate the performance of individuals in virtual career paths to assess the career adaptability and development potential of individuals in different career environments; the career adaptability and development potential sub-model is obtained based on the adaptability and development potential of individuals in different career environments. .

8. The talent information processing system based on professional quality and big data analysis according to claim 7 is characterized in that: The determination of the individual comprehensive ability assessment model, the occupational adaptability and development potential sub-model, and the social network and influence assessment sub-model based on the unstructured features related to occupational literacy is configured as follows: the occupational adaptability and development potential sub-model is used to predict the individual's future growth trajectory and occupational development potential; Based on unstructured characteristics related to professionalism The career adaptability and development potential sub-model predicts the individual's future growth trajectory and career development potential. The social network analysis algorithm and natural language processing algorithm are combined to analyze the interaction pattern of individuals in social media and professional forums to determine the career adaptability and development potential sub-model ; Based on the influence and social ability of the individual in the professional network obtained by evaluating the career adaptability and development potential sub-model, the career adaptability and development potential sub-model is updated to obtain an updated career adaptability and development potential sub-model. .

9. The talent information processing system based on professional quality and big data analysis according to claim 8 is characterized in that: The talent matching algorithm based on graph theory is determined according to the output results of the multi-level professional quality assessment and prediction model and is configured as follows: Constructing an initial graph model; wherein the initial graph model includes talent nodes and position nodes; the node characteristics of each talent node include the output of the individual comprehensive ability evaluation model, the career adaptability and development potential sub-model, and the social network and influence evaluation sub-model; the node characteristics of each position node include position description, required skills, and social influence; The first similarity between the talent node and the position node is calculated by combining the word frequency-inverse document frequency algorithm with the word embedding algorithm.

10. The talent information processing system based on professional quality and big data analysis according to claim 9 is characterized in that: The talent matching algorithm based on graph theory is determined according to the output results of the multi-level professional quality assessment and prediction model, and is configured to: analyze the second similarity of the edges between the talent nodes and the third similarity of the edges between the position nodes by analyzing the relationship between the talent nodes and the correlation between the position nodes; Calculating an association matrix in graph matching according to the first similarity, the second similarity, and the third similarity; Combining the decomposition graph matching algorithm and the probabilistic graph matching algorithm, the talent matching algorithm is constructed according to the association matrix in the graph matching; The output results of the multi-level professional quality assessment and prediction model are incorporated into the talent matching algorithm as weighted factors.

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