Method and device for talent value model

Abstract

The invention discloses a talent value model method and device, and belongs to the technical field of information processing, and the method comprises the steps: obtaining talent sample data and carrying out the preprocessing, carrying out the talent value prediction according to a preprocessed talent data set, simulating a targeted AR scene according to a talent value prediction result, and evaluating the behavior score of a talent in a specific scene. According to the method, talent model prediction results and behavior scores in AR are combined to perform talent comprehensive evaluation, the talent and post matching degree is optimized through a quantum heuristic optimization algorithm, the talent and post matching degree is intelligently optimized through the quantum heuristic optimization algorithm, the Hamiltonian is constructed and the minimum energy state is solved, and the matching degree of the talent and the post is optimized. According to the method, the optimal or approximately optimal talent and post matching scheme can be efficiently found, so that the matching degree and the overall efficiency are improved, the optimal matching scheme can be found in a short time, and higher efficiency and accuracy are achieved.

Description

Technical Field

[0001] The present invention belongs to the field of information processing technology, and specifically refers to a method and device for a talent value model. Background Art

[0002] In a rapidly changing environment, companies are increasingly demanding talent, but traditional talent assessment methods often focus on past performance and static skills, and cannot fully reflect an individual's potential and ability to adapt to the future;

[0003] However, there are still certain defects in the existing talent value model methods. The existing talent value model methods rely on traditional algorithms. These methods are inefficient and time-consuming when processing large-scale data sets, and it is difficult to find the global optimal solution. For large-scale talent pools and job requirements, traditional methods are difficult to quickly find the optimal or approximately optimal matching solution. Therefore, a talent value model method and device are proposed. Summary of the invention

[0004] The purpose of the present invention is to provide a method and device for a talent value model to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solution: a method and device for a talent value model, comprising the following steps:

[0006] S1. Obtain talent sample data and perform preprocessing;

[0007] S2. Predicting talent value based on the preprocessed talent data set;

[0008] S3. Based on the talent value prediction results, simulate targeted AR scenarios and evaluate the behavioral scores of talents in specific scenarios;

[0009] S4. Conduct comprehensive talent assessment based on the talent model prediction results and the behavioral scores in AR;

[0010] S5. Comprehensive talent assessment optimizes the matching between talent and position through quantum heuristics;

[0011] S6. Automatically adjust the talent model based on the quantum-inspired optimization results.

[0012] Among them, S1 obtains talent sample data, cleans the acquired talent sample data, extracts text information from unstructured data sources, divides the extracted text data into separate words, marks the part of speech and named entity recognition for each word to identify key entities, converts and encodes the text data, extracts key feature data from the preprocessed text data, and integrates the feature data into a talent data set.

[0013] The quality and accuracy of input data are ensured through comprehensive cleaning and preprocessing of talent sample data, including data cleaning, text information extraction, vocabulary segmentation, part-of-speech tagging, named entity recognition, data conversion and encoding, etc.

[0014] Wherein, in S2, the characteristic vector of the talent dataset T is assumed to be F = (f1, f2, ..., f i ), the weight vector is W = (w1,w2,…,w n ), the threshold vector is T=(t1,t2,…,t i ), the talent value prediction formula is:

[0015]

[0016] In the formula, V(T) represents the value score of talent T, t i represents the threshold of the i-th dimension, n represents the number of evaluation dimensions, represents the total weighted score, w i represents the weight of the i-th dimension, f i represents the score of the i-th dimension, with a value range of [0, 1], 0 represents the worst, 1 represents the best, and p i represents the penalty coefficient, represents the indicator function, when f i <t i , the indicator function returns 1, otherwise it returns 0. Represents the total penalty score. The total weighted score minus the total penalty score gives the final talent value score.

[0017] By building a complex talent value prediction model, taking into account multiple evaluation dimensions, and introducing mechanisms such as weights, thresholds, and penalty coefficients, the evaluation of talent value is made more comprehensive and detailed. This multi-dimensional evaluation method can more accurately reflect the actual value of talents.

[0018] Among them, S3 performs personalized adjustment on the AR scene according to the talent value prediction result obtained in S2. Suppose that each scene S i Each corresponds to a set of evaluation indicators M i ={m i1 ,m i2 ,...,m ij},m ij represents the jth evaluation index in the i-th scene. In each scene S i Collect behavioral data i , from the behavioral data D i Extract feature F i ={f i1 ,f i2 ,...,f ij}, the behavior evaluation implementation formula is:

[0019]

[0020] In the formula, E i (F i ) represents scene S i Medium Feature F i Rating, w ij represents the weight of the jth feature in scene i, f ij Indicates that the behavior data D i The jth feature extracted from i Calculate a talent behavior score V(S i ), the implementation formula is:

[0021] V(S i )=E i (F i ).

[0022] By simulating targeted AR scenarios and evaluating the behavioral performance of talents in specific scenarios, it can more intuitively reflect the ability and adaptability of talents in actual work. At the same time, by extracting features from behavioral data and scoring them, the evaluation results can be more specific.

[0023] In which, S4 combines the talent behavior score obtained in S3 and the talent value prediction result obtained in S2 into a comprehensive talent evaluation, and the implementation formula is:

[0024]

[0025] In the formula, V′(T) represents the comprehensive evaluation of talents, α i represents the weighting coefficient of the i-th scene.

[0026] In S5, let the comprehensive talent evaluation vector V′(T)={v′1,v′2,...,v′ n}, suppose a job demand matrix R ij Form an m*n, and define the objective function Q as:

[0027]

[0028] In the formula, V′(T i ) represents the talent evaluation value of the i-th talent, R ij represents the matching degree between the i-th talent and the j-th position, w j represents the weight coefficient, m represents the number of positions, and n represents the number of talents.

[0029] By combining the talent value prediction results with the behavioral scores in the AR scene, a comprehensive talent assessment is conducted, and the matching degree between talent and position is optimized through the quantum heuristic algorithm. This not only takes into account the ability and value of the talent, but also the actual needs and characteristics of the position, thus achieving more accurate talent allocation.

[0030] Wherein, the S5 constructs a Hamiltonian according to the defined objective function, and the Hamiltonian is proportional to the objective function, and the formula is:

[0031]

[0032] In the formula, Z ij Represents 2q ij -1,q ij represents quantum bits, the matching degree between talent i and position j, q ij =1 indicates a match, q ij =0 means no match, w j represents the weight of the j-th position, H represents the Hamiltonian, and its lowest energy state corresponds to the minimum value of the objective function.

[0033] Wherein, the S6 automatically adjusts the talent model according to the quantum heuristic optimization result, and when the demand for a position is overestimated, the demand value of the position is reduced, and when the demand for a position is underestimated, the demand value of the position is increased.

[0034] Among them, a device for a talent value model is characterized by comprising a data collector, a data storage device and a processor;

[0035] The data collector is used to obtain talent sample data and perform preliminary data processing;

[0036] The data storage device is used to store talent sample data and subsequent data results;

[0037] The processor is used for data preprocessing and executing the talent value model method of any one of claims 1 to 8.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] 1. In order to solve the problems of inaccurate talent value assessment and low matching degree between talents and positions, and to improve the accuracy and comprehensiveness of talent assessment, the present invention uses a quantum heuristic optimization algorithm to intelligently optimize the matching degree between talents and positions. By constructing a Hamiltonian and solving its lowest energy state, the optimal or approximately optimal talent and position matching scheme can be efficiently found, thereby improving the matching degree and overall efficiency. The best matching scheme can be found in a short time, and compared with traditional matching methods, it has higher efficiency and accuracy;

[0040] 2. The present invention realizes a multi-dimensional comprehensive evaluation of talent value by combining the preprocessing and feature extraction of talent sample data. It not only considers the basic characteristics of talents, but also introduces thresholds and penalty coefficients to more finely evaluate the value of talents, making the evaluation results more comprehensive and accurate.

[0041] 3. The present invention simulates targeted work scenarios based on the predicted results of talent value, allowing talents to demonstrate their actual abilities and behaviors in a virtual environment. Compared with traditional theoretical tests or interviews, this can more realistically reflect the performance of talents in actual work and improve the accuracy and effectiveness of the assessment;

[0042] 4. The present invention automatically adjusts the talent model according to the quantum-inspired optimization results, so that it can continuously adapt to new data and scenarios and maintain accuracy and effectiveness. Compared with static models, this dynamic adjustment method is more flexible. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 This is an operation flow chart of a method for a talent value model of the present invention;

[0044] Figure 2 It is an operation flow chart of talent value prediction of a talent value model method of the present invention;

[0045] Figure 3 A schematic diagram of the structure of a quantum heuristic method for a talent value model of the present invention;

[0046] Figure 4 The figure is a schematic diagram of the structure of a device for a talent value model of the present invention. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] Example

[0049] See also Figure 1-Figure 4 As shown, the present invention provides a technical solution: comprising the following steps:

[0050] S1. Obtain talent sample data and perform preprocessing;

[0051] S2. Predicting talent value based on the preprocessed talent data set;

[0052] S3. Based on the talent value prediction results, simulate targeted AR scenarios and evaluate the behavioral scores of talents in specific scenarios;

[0053] S4. Conduct comprehensive talent assessment based on the talent model prediction results and the behavioral scores in AR;

[0054] S5. Comprehensive talent assessment optimizes the matching between talent and position through quantum heuristics;

[0055] S6. Automatically adjust the talent model based on the quantum-inspired optimization results.

[0056] Among them, S1 obtains talent sample data, cleans the acquired talent sample data, extracts text information from unstructured data sources, divides the extracted text data into separate words, marks the part of speech and named entity recognition for each word to identify key entities, converts and encodes the text data, extracts key feature data from the preprocessed text data, and integrates the feature data into a talent data set.

[0057] The quality and accuracy of input data are ensured through comprehensive cleaning and preprocessing of talent sample data, including data cleaning, text information extraction, vocabulary segmentation, part-of-speech tagging, named entity recognition, data conversion and encoding, etc.

[0058] Wherein, in S2, the characteristic vector of the talent dataset T is assumed to be F = (f1, f2, ..., f i ), the weight vector is W = (w1,w2,…,w n ), the threshold vector is T=(t1,t2,…,t i ), the talent value prediction formula is:

[0059]

[0060] In the formula, V(T) represents the value score of talent T, t i represents the threshold of the i-th dimension, n represents the number of evaluation dimensions, represents the total weighted score, w i represents the weight of the i-th dimension, f i represents the score of the i-th dimension, with a value range of [0, 1], 0 represents the worst, 1 represents the best, and p i represents the penalty coefficient, represents the indicator function, when f i <t i , the indicator function returns 1, otherwise it returns 0. Represents the total penalty score. The total weighted score minus the total penalty score gives the final talent value score.

[0061] By building a complex talent value prediction model, taking into account multiple evaluation dimensions, and introducing mechanisms such as weights, thresholds, and penalty coefficients, the evaluation of talent value is made more comprehensive and detailed. This multi-dimensional evaluation method can more accurately reflect the actual value of talents.

[0062] Among them, S3 performs personalized adjustment on the AR scene according to the talent value prediction result obtained in S2. Suppose that each scene S i Each corresponds to a set of evaluation indicators M i ={m i1 ,m i2 ,...,m ij},m ij represents the jth evaluation index in the i-th scene. In each scene S i Collect behavioral data i , from the behavioral data D i Extract feature F i ={f i1 ,f i2 ,...,f ij}, the behavior evaluation implementation formula is:

[0063]

[0064] In the formula, E i (F i ) represents scene S i Medium Feature F i Rating, w ij represents the weight of the jth feature in scene i, f ij Indicates that the behavior data D i The jth feature extracted from i Calculate a talent behavior score V(S i ), the implementation formula is:

[0065] V(S i )=E i (F i ).

[0066] By simulating targeted AR scenarios and evaluating the behavioral performance of talents in specific scenarios, it can more intuitively reflect the ability and adaptability of talents in actual work. At the same time, by extracting features from behavioral data and scoring them, the evaluation results can be more specific.

[0067] In which, S4 combines the talent behavior score obtained in S3 and the talent value prediction result obtained in S2 into a comprehensive talent evaluation, and the implementation formula is:

[0068]

[0069] In the formula, V′(T) represents the comprehensive evaluation of talents, α i represents the weighting coefficient of the i-th scene.

[0070] In S5, let the comprehensive talent evaluation vector V′(T)={v′1,v′2,...,v′ n}, suppose a job demand matrix R ij Form an m*n, and define the objective function Q as:

[0071]

[0072] In the formula, V′(T i ) represents the talent evaluation value of the i-th talent, R ij represents the matching degree between the i-th talent and the j-th position, w j represents the weight coefficient, m represents the number of positions, and n represents the number of talents.

[0073] By combining the talent value prediction results with the behavioral scores in the AR scene, a comprehensive talent assessment is conducted, and the matching degree between talent and position is optimized through the quantum heuristic algorithm. This not only takes into account the ability and value of the talent, but also the actual needs and characteristics of the position, thus achieving more accurate talent allocation.

[0074] Wherein, the S5 constructs a Hamiltonian according to the defined objective function, and the Hamiltonian is proportional to the objective function, and the formula is:

[0075]

[0076] In the formula, Z ij Represents 2q ij -1,q ij represents quantum bits, the matching degree between talent i and position j, q ij =1 indicates a match, q ij =0 means no match, w j represents the weight of the j-th position, H represents the Hamiltonian, and its lowest energy state corresponds to the minimum value of the objective function.

[0077] Wherein, the S6 automatically adjusts the talent model according to the quantum heuristic optimization result, and when the demand for a position is overestimated, the demand value of the position is reduced, and when the demand for a position is underestimated, the demand value of the position is increased.

[0078] Among them, a device for a talent value model is characterized by comprising a data collector, a data storage device and a processor;

[0079] The data collector is used to obtain talent sample data and perform preliminary data processing;

[0080] The data storage device is used to store talent sample data and subsequent data results;

[0081] The processor is used for data preprocessing and executing the talent value model method of any one of claims 1 to 8.

[0082] In this example, specifically: S2, assuming that the feature vector of the talent data set T is F = (f1, f2, ..., f i ), the weight vector is W = (w1,w2,…,w n ), the threshold vector is T=(t1,t2,…,t i ), the talent value prediction formula is:

[0083]

[0084] In the formula, V(T) represents the value score of talent T, t i represents the threshold of the i-th dimension, n represents the number of evaluation dimensions, represents the total weighted score, w i represents the weight of the i-th dimension, f i represents the score of the i-th dimension, with a value range of [0, 1], 0 represents the worst, 1 represents the best, and p i represents the penalty coefficient, represents the indicator function, when f i <t i , the indicator function returns 1, otherwise it returns 0. Represents the total penalty score. The total weighted score minus the total penalty score gives the final talent value score.

[0085] In this example, specifically: S4 combines the talent behavior score obtained in S3 and the talent value prediction result obtained in S2 into a comprehensive talent evaluation, and the implementation formula is:

[0086]

[0087] In the formula, V′(T) represents the comprehensive evaluation of talents, α i represents the weighting coefficient of the i-th scene.

[0088] In S5, let the comprehensive talent evaluation vector V′(T)={v′1,v′2,...,v′ n}, suppose a job demand matrix R ij Form an m*n, and define the objective function Q as:

[0089]

[0090] In the formula, V′(T i) represents the talent evaluation value of the i-th talent, R ij represents the matching degree between the i-th talent and the j-th position, w j represents the weight coefficient, m represents the number of positions, and n represents the number of talents.

[0091] In this example, specifically: S5 constructs a Hamiltonian according to the defined objective function, and the Hamiltonian is proportional to the objective function, and the formula is:

[0092]

[0093] In the formula, Z ij Represents 2q ij -1,q ij represents quantum bits, the matching degree between talent i and position j, q ij =1 indicates a match, q ij =0 means no match, w j represents the weight of the j-th position, H represents the Hamiltonian, and its lowest energy state corresponds to the minimum value of the objective function.

[0094] Wherein, the S6 automatically adjusts the talent model according to the quantum heuristic optimization result, and when the demand for a position is overestimated, the demand value of the position is reduced, and when the demand for a position is underestimated, the demand value of the position is increased.

[0095] Among them, a device for a talent value model is characterized by comprising a data collector, a data storage device and a processor;

[0096] The data collector is used to obtain talent sample data and perform preliminary data processing;

[0097] The data storage device is used to store talent sample data and subsequent data results;

[0098] The processor is used for data preprocessing and executing the talent value model method of any one of claims 1 to 8.

[0099] Working principle: By collecting talent sample data from various data sources, cleaning the talent sample data, extracting text information from unstructured data, performing word segmentation, part-of-speech tagging and named entity recognition to identify key entities, and converting text data into numerical features;

[0100] Through the preprocessed talent data set, a model for talent value prediction is constructed by setting weight vectors, threshold vectors and penalty coefficients. The model is used to predict the value of talents, calculate their total weighted scores, and consider the penalty coefficients and indicator functions to adjust the final talent value scores. According to the results of talent value prediction, targeted AR scenarios are simulated for talents, and the behavioral data of talents are collected in AR scenarios. Features are extracted from the behavioral data, and the talent behavior scores of each scenario are calculated through behavioral evaluation. The behavioral scores in the AR scenarios are combined with the talent value prediction results, and the comprehensive evaluation scores of talents are calculated by weighted summation. An objective function is constructed to maximize the matching degree between talents and positions. By defining the position demand matrix and the talent evaluation value, quantum heuristics are used to find the optimal matching solution. According to the optimization results of quantum heuristics, the talent model is automatically adjusted.

[0101] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

[0102] The present invention and its embodiments are described above, and such description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design a structure and embodiment similar to the technical solution without creativity, which should belong to the protection scope of the present invention.

Claims

1. A method for a talent value model, characterized in that: The following steps are involved: S1. Obtain talent sample data and perform preprocessing; S2. Predicting talent value based on the preprocessed talent data set; S3. Based on the talent value prediction results, simulate targeted AR scenarios and evaluate the behavioral scores of talents in specific scenarios; S4. Conduct comprehensive talent assessment based on the talent model prediction results and the behavioral scores in AR; S5. Comprehensive talent assessment optimizes the matching between talent and position through quantum heuristics; S6. Automatically adjust the talent model based on the quantum-inspired optimization results.

2. The method of a talent value model according to claim 1, characterized in that: The S1 obtains talent sample data, performs data cleaning on the obtained talent sample data, extracts text information from unstructured data sources, divides the extracted text data into separate words, annotates the part of speech and performs named entity recognition on each word to identify key entities, performs data conversion and encoding on the text data, extracts key feature data from the preprocessed text data, and integrates the feature data into a talent data set.

3. The method of a talent value model according to claim 1, characterized in that: In S2, the characteristic vector of the talent data set T is assumed to be F = (f1, f2, ..., fi), the weight vector is W = (w1, w2, ..., wn), the threshold vector is T = (t1, t2, ..., ti), and the talent value prediction formula is: In the formula, V(T) represents the value score of talent T, t i represents the threshold of the i-th dimension, n represents the number of evaluation dimensions, represents the total weighted score, w i represents the weight of the i-th dimension, f i represents the score of the i-th dimension, with a value range of [0, 1], 0 represents the worst, 1 represents the best, and p i represents the penalty coefficient, represents the indicator function, when f i <t i , the indicator function returns 1, otherwise it returns 0. Represents the total penalty score. The total weighted score minus the total penalty score gives the final talent value score.

4. The method of a talent value model according to claim 1, characterized in that: S3, according to the talent value prediction result obtained in S2, makes personalized adjustments to the AR scene. Suppose that each scene S i Each corresponds to a set of evaluation indicators M i ={m i1 ,m i2 ,...,m ij },m ij represents the jth evaluation index in the i-th scene. In each scene S i Collect behavioral data i , from the behavioral data D i Extract feature F i ={f i1 ,f i2 ,...,f ij }, the behavior evaluation implementation formula is: In the formula, E i (F i ) represents scene S i Medium Feature F i Rating, w ij represents the weight of the jth feature in scene i, f ij Indicates that the behavior data D i The jth feature extracted from For each scene S i Calculate a talent behavior score V(S i ), the implementation formula is: V(S i )=E i (F i )。 5. The method of a talent value model according to claim 1, characterized in that: S4 combines the talent behavior score obtained in S3 and the talent value prediction result obtained in S2 into a comprehensive talent evaluation, and the implementation formula is: In the formula, V′(T) represents the comprehensive evaluation of talents, α i represents the weighting coefficient of the i-th scene.

6. The method of a talent value model according to claim 1, characterized in that: In S5, let the comprehensive talent evaluation vector V′(T) = {v′1, v′2, ..., v′ n }, suppose a job demand matrix R ij Form an m*n, and define the objective function Q as: In the formula, V′(T i ) represents the talent evaluation value of the i-th talent, R ij represents the matching degree between the i-th talent and the j-th position, w j represents the weight coefficient, m represents the number of positions, and n represents the number of talents.

7. The method of a talent value model according to claim 6, characterized in that: S5, according to the defined objective function, constructs a Hamiltonian, which is proportional to the objective function, and the formula is: In the formula, Z ij Represents 2q ij -1,q ij represents quantum bits, the matching degree between talent i and position j, q ij =1 indicates a match, q ij =0 means no match, w j represents the weight of the j-th position, H represents the Hamiltonian, and its lowest energy state corresponds to the minimum value of the objective function.

8. The method of a talent value model according to claim 1, characterized in that: The S6 automatically adjusts the talent model according to the quantum heuristic optimization result. When the demand for a position is overestimated, the demand value of the position is reduced. When the demand for a position is underestimated, the demand value of the position is increased.

9. A device for a talent value model, characterized in that: It includes a data collector, a data storage device and a processor; The data collector is used to obtain talent sample data and perform preliminary data processing; The data storage device is used to store talent sample data and subsequent data results; The processor is used for data preprocessing and executing the talent value model method of any one of claims 1 to 8.

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