CN117787814A - Talent element ability assessment method and system based on AI - Google Patents
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Abstract
The invention relates to the technical field of capability assessment, in particular to a talent element capability assessment method and system based on AI. The method comprises the steps of definitely formulating evaluation indexes such as technical capability, communication capability, leading potential and innovative thinking, collecting multi-channel data and storing the multi-channel data in a safety database, integrating and processing the data through natural language processing, creating labels for different qualities and capabilities, extracting key features, constructing an evaluation model by using deep learning and self-adaptive algorithm, evaluating evaluated personnel and generating personalized advice, and simultaneously adopting differential privacy technology to ensure data protection. The invention provides an intelligent, comprehensive and personalized solution for talent assessment, which is helpful for individuals and organizations to better understand, cultivate and manage talents.
Description
Technical Field
The invention relates to the technical field of capability assessment, in particular to a talent element capability assessment method and system based on AI.
Background
Talent quality capability assessment is always a key link for organizing recruitment and professional development. Currently, traditional assessment methods rely mainly on interviews, histories and standardized tests. Although these methods are effective to some extent, there are some problems: the traditional talent assessment system may have subjective and unfair problems; when large-scale employee data are collected and analyzed, hidden danger exists in the problems of data privacy and safety; data quality problems may lead to misleading assessment results.
Disclosure of Invention
In order to solve the above problems, the present invention provides a talent element ability evaluation method and system based on AI.
In order to achieve the above purpose, the invention adopts the following technical scheme:
on the one hand, the talent element ability assessment method based on AI comprises the following steps:
formulating clear evaluation indexes, wherein the evaluation indexes comprise evaluation technical capability, communication capability, leading potential and innovative thinking;
collecting talent quality data of a plurality of channels, and storing the talent quality data into a safe database;
integrating and processing the talent quality data through natural language processing;
creating labels for different quality and capacities, and extracting key features from the processed talent quality data;
based on the extraction and evaluation indexes of the features, constructing an evaluation model by using a deep learning and self-adaptive algorithm;
using the established evaluation model to evaluate the evaluated personnel, generating a corresponding evaluation report, and generating personalized suggestions according to the evaluation report;
and data protection is carried out on talent quality data by using a differential privacy technology.
Further, the talent quality data includes a resume, an interview record, performance data, educational background, and a skill record.
Further, the data integration and processing are performed on the talent quality data through natural language processing, and the method comprises the following steps:
acquiring talent quality data;
cleaning and processing the collected talent quality data;
determining entities in the text using natural language processing techniques;
carrying out emotion analysis on the text data;
classifying the text data according to different evaluation indexes;
the extracted features and classification information are integrated into one data set.
Further, the construction of the evaluation model by using the deep learning and adaptive algorithm comprises the following steps:
defining a target and an evaluation index of an evaluation model;
collecting and preprocessing talent quality data, and performing data cleaning, missing value filling and standardization treatment;
designing a neural network model structure, and defining a network layer, an activation function and an output layer;
training the neural network model by using the marked talent quality data;
using a cross entropy loss function and an optimization algorithm to guide a model training process and training a neural network model by using the marking data;
adjusting parameters and structures of the model by using an adaptive algorithm;
dividing the data into a training set and a verification set, and considering the weights and the relevance of different evaluation indexes;
the performance of the model is evaluated using the validation set.
Still further, the adaptive algorithm comprises the steps of:
constructing an initial neural network model;
starting an iteration process of the self-adaptive algorithm, and analyzing talent quality data;
according to the analysis result of the data, the parameters of the model are adjusted;
dynamically adjusting the learning rate according to the progress of the training using a learning rate adjustment formula;
introducing regularization terms into the loss function of the model;
gradient clipping is used to control the magnitude of the gradient;
after each adaptation iteration, the performance of the model on the validation set is evaluated.
Further, the learning rate adjustment formula specifically includes:
，
wherein the initial learning rate is an initial value, k is an adjustment parameter, and epoch represents the number of iterative rounds of training.
Further, the data protection of talent quality data by using the differential privacy technology comprises the following steps:
acquiring talent quality data;
anonymizing talent quality data;
determining parameters of differential privacy;
when a user queries data, random noise is added to a query result, and the noise generation mode comprises Laplacian noise or Gaussian noise;
issuing a query result after privacy protection treatment;
and adjusting the differential privacy parameters according to the result of the privacy analysis to achieve proper balance.
Further, the parameters of the differential privacy are epsilon and delta;
epsilon represents privacy parameters of differential privacy, the level of privacy protection is controlled, smaller epsilon provides stronger privacy protection, and epsilon is a positive number;
delta represents the probability of failure in a query for controlling the differential privacy algorithm, delta is typically very close to zero and can be considered a small value.
On the other hand, the talent element capability assessment system based on AI comprises an index making module, a data collecting module, a data integrating and processing module, a label creating module, an evaluating module, a data storage module and a privacy protecting module; the data storage module is in communication connection with the index making module, the data collecting module, the data integrating and processing module, the label creating module, the evaluating module and the privacy protecting module;
the index making module is used for making clear evaluation indexes, wherein the evaluation indexes comprise evaluation technical capability, communication capability, leading potential and innovation thinking;
the data collection module is used for collecting talent quality data from a plurality of channels and storing the data into a safe database;
the data integration and processing module is used for cleaning, entity identification and emotion analysis of the collected talent quality data by using a natural language processing technology, and classifying the data according to different evaluation indexes;
the label creation module is used for creating labels for different qualities and capacities and extracting key characteristics of the processed talent quality data;
the evaluation module is used for constructing a talent element ability evaluation model by using a deep learning and self-adaptive algorithm, wherein the self-adaptive algorithm comprises the steps of neural network construction, parameter adjustment, learning rate adjustment, regularization and gradient cutting, the established evaluation model is used for evaluating the personnel to be evaluated, a corresponding evaluation report is generated, and personalized advice is generated according to the evaluation report;
the data storage module is used for storing various data in the system;
the privacy protection module is used for protecting the talent quality data by using a differential privacy technology.
The invention has the beneficial effects that:
by collecting talent quality data from multiple channels, the method ensures the diversity and the universality of the data and reduces the possibility of sample deviation. This helps to more accurately reflect the true quality and ability of the person being evaluated. The data is integrated and processed by using natural language processing technology, which is helpful for improving the quality and usability of the data. This can reduce the workload of manually processing data and improve efficiency.
According to the method, the personalized suggestions can be generated for each person to be evaluated by establishing the evaluation model. This means that everyone can get targeted development advice based on his unique quality and ability, contributing to the growth and professional development of the individual. The self-adaptive algorithm is used for constructing an evaluation model, and dynamic adjustment and improvement of the model can be carried out according to the continuously accumulated data, so that the accuracy and the prediction capability of evaluation are improved.
According to the method, the differential privacy technology is adopted to protect talent quality data, and the safety and privacy of the data are ensured. This helps to build trust, making talents willing to share their data for evaluation, while preserving their privacy.
Drawings
Fig. 1 is a schematic flow chart of an AI-based talent mass-energy assessment method of the present invention.
Fig. 2 is a flow chart of the differential privacy technique of the present invention.
Fig. 3 is a schematic block diagram of an AI-based talent ability evaluation system of the present invention.
Detailed Description
Referring to fig. 1-3, the present invention relates to a method and a system for evaluating talent element ability based on AI.
Example 1
An AI-based talent element ability assessment method comprises the following steps:
s1, formulating clear evaluation indexes, wherein the evaluation indexes comprise evaluation technical capability, communication capability, leading potential and innovation thinking;
s2, collecting talent quality data of a plurality of channels, and storing the talent quality data into a safe database;
wherein the talent quality data includes resume, interview records, performance data, educational background, and skill records.
S3, integrating and processing the talent quality data through natural language processing;
the talent quality data is integrated and processed through natural language processing, and the method comprises the following steps:
s31: acquiring talent quality data;
s32: cleaning and processing the collected talent quality data;
s33: determining entities in the text using natural language processing techniques;
specifically, named Entity Recognition (NER) techniques are used to identify important entities in text such as person names, place names, organization names, and the like.
S34: carrying out emotion analysis on the text data;
in particular, emotion analysis techniques are used to determine emotion tendencies expressed in text, such as positive, negative, or neutral. For example, emotion analysis algorithms are used to analyze the emotion colors in the self-evaluation and work experience expressed by the candidate in the resume.
S35: classifying the text data according to different evaluation indexes;
specifically, the text data is classified according to a pre-defined evaluation index, such as skill level, work experience, academic background, and the like. For example, skill levels in the resume are rated according to keyword matching and semantic similarity calculations.
S36: the extracted features and classification information are integrated into one data set.
S4, creating labels for different quality and capacities, and extracting key features of the processed talent quality data;
the method comprises the following steps of:
s41: defining a target and an evaluation index of an evaluation model;
s42: collecting and preprocessing talent quality data, and performing data cleaning, missing value filling and standardization treatment;
s43: designing a neural network model structure, and defining a network layer, an activation function and an output layer;
specifically, the network structure of the talent element ability assessment model is constructed based on a neural network algorithm in deep learning, such as a multi-layer perceptron (MLP), a Convolutional Neural Network (CNN), a cyclic neural network (RNN), and the like.
It should be noted that the network hierarchy includes an input layer and a hidden layer; the number of nodes of the input layer should be matched with the number of features. For example, if our dataset contains 10 different features (e.g., educational background, age of work experience, skill score, etc.), then the input layer should have 10 nodes. The hidden layer is the core of the neural network and is responsible for learning patterns and relationships in the data. One starting point might be two hidden layers, each layer having 64 nodes. This configuration can be adjusted according to the performance of the model: if the model is under fitted, the number of hidden layers or the number of nodes per layer can be increased; if the model is over-fitted, the number of layers or nodes can be reduced. For hidden layers, a ReLU activation function is typically used.
S44: training the neural network model by using the marked talent quality data;
specifically, the weights and biases of the neural network model need to be randomly initialized, and during forward propagation, input data is passed through the network from the input layer to the output layer. At each node, the output of the previous layer is multiplied by the weight of the current layer, biased, and then an activation function is applied. At the output layer, the predicted result of the model is compared with the actual label, and a loss value is calculated. The weights and biases for each layer are updated using the gradient of the loss function back-propagation through the network. This step is accomplished by gradient descent or variants thereof (e.g., adam optimizers), and these algorithms adjust parameters based on the gradient of the loss function to reduce the loss.
S45: using a cross entropy loss function and an optimization algorithm to guide a model training process and training a neural network model by using the marking data;
s46: adjusting parameters and structures of the model by using an adaptive algorithm;
wherein the adaptive algorithm comprises the steps of:
constructing an initial neural network model;
specifically, an initial structure of the neural network is designed, including the number of layers of the network, the number of nodes of each layer, an activation function, and the like. A multi-layer perceptron model is constructed with two hidden layers and a ReLU is selected as the activation function.
Starting an iteration process of the self-adaptive algorithm, and analyzing talent quality data;
specifically, the talent quality data is used as a training set, training is performed using an initial model, and performance of the model on the training set is analyzed. And observing the change condition of the loss function of the model on the training set and the improvement condition of the accuracy.
According to the analysis result of the data, the parameters of the model are adjusted;
dynamically adjusting the learning rate according to the progress of the training using a learning rate adjustment formula;
specifically, the learning rate adjustment formula specifically includes:
，
wherein the initial learning rate is an initial value, k is an adjustment parameter, and epoch represents the number of iterative rounds of training.
Introducing regularization terms into the loss function of the model;
specifically, to prevent overfitting, regularization terms, such as L1 regularization or L2 regularization, are introduced into the loss function of the model. Regularization terms are added to the loss function to control the complexity of the model and avoid overfitting of the model on the training set.
Gradient clipping is used to control the magnitude of the gradient;
after each adaptation iteration, the performance of the model on the validation set is evaluated.
S47: training the model by using the marked talent quality data;
s48: dividing the data into a training set and a verification set, and considering the weights and the relevance of different evaluation indexes;
specifically, the data set is divided into a training set and a verification set according to a certain proportion, so that the generalization capability of the model is ensured. Weights and correlations of different evaluation indexes (such as skill matching degree, working experience and the like) are considered to reasonably design a training objective function.
S49: the performance of the model is evaluated using the validation set.
Specifically, indexes such as accuracy, precision, recall rate and the like of the model on the verification set are calculated, the overall performance of the model is estimated, and the model is further optimized.
S5, based on the extraction and evaluation indexes of the features, constructing an evaluation model by using a deep learning and self-adaptive algorithm;
s6, evaluating the evaluated personnel by using the established evaluation model, generating a corresponding evaluation report, and generating personalized suggestions according to the evaluation report;
s7, performing data protection on talent quality data by using a differential privacy technology;
the differential privacy technology is used for protecting talent quality data, and the differential privacy technology comprises the following steps:
s71: acquiring talent quality data;
s72: anonymizing talent quality data;
specifically, the personal sensitive information is subjected to desensitization treatment, and sensitive information such as names, identification card numbers and the like is replaced by unique identifiers. The data after anonymization processing can not be restored to the original personal identity information.
S73: determining parameters of differential privacy;
specifically, the parameters of the differential privacy are epsilon and delta;
epsilon represents privacy parameters of differential privacy, the level of privacy protection is controlled, smaller epsilon provides stronger privacy protection, and epsilon is a positive number;
delta represents the probability of failure in a query for controlling the differential privacy algorithm, delta is typically very close to zero and can be considered a small value.
S74: when a user queries data, random noise is added to a query result, and the noise generation mode comprises Laplacian noise or Gaussian noise;
s75: issuing a query result after privacy protection treatment;
s76: and adjusting the differential privacy parameters according to the result of the privacy analysis to achieve proper balance.
In this embodiment, the method can comprehensively evaluate the technical ability, communication ability, leadership potential and innovative thinking of the candidate by formulating a clear evaluation index. Through natural language processing technology, the method can automatically integrate, clean, identify and emotion analyze talent quality data, thereby reducing the burden of manually processing the data and improving the efficiency. The deep learning and self-adaptive algorithm is used for constructing the evaluation model, so that complex association and characteristics can be better captured, and the accuracy and reliability of the evaluation model are improved. The parameter adaptive adjustment of the model also helps to optimize the model performance. The generated assessment reports may provide personalized advice that makes them more in line with the desired job requirements.
By using the differential privacy technology, the method ensures the privacy and safety of talent quality data. By anonymizing the data and adding noise, meaningful data analysis can be performed while protecting personal information. This helps to comply with privacy regulations and establish trust in the data processing.
Example 2
The AI-based talent ability assessment method according to embodiment 1, wherein an AI-based talent ability assessment system includes an index formulation module, a data collection module, a data integration and processing module, a tag creation module, an evaluation module, a data storage module, and a privacy protection module; the data storage module is in communication connection with the index making module, the data collecting module, the data integrating and processing module, the label creating module, the evaluating module and the privacy protecting module;
the index formulation module is used for formulating clear evaluation indexes, wherein the evaluation indexes comprise evaluation technical capability, communication capability, leading potential and innovation thinking.
The data collection module is used for collecting talent quality data from a plurality of channels and storing the data into a safe database.
The data integration and processing module is used for cleaning, entity identification and emotion analysis of the collected talent quality data by using a natural language processing technology, and classifying the data according to different evaluation indexes.
The label creation module is used for creating labels for different qualities and capacities and extracting key characteristics of the processed talent quality data.
The evaluation module is used for constructing a talent element ability evaluation model by using deep learning and a self-adaptive algorithm, wherein the self-adaptive algorithm comprises the steps of neural network construction, parameter adjustment, learning rate adjustment, regularization, gradient cutting and the like, the established evaluation model is used for evaluating the personnel to be evaluated, a corresponding evaluation report is generated, and personalized advice is generated according to the evaluation report.
The data storage module is used for storing various data in the system;
the privacy protection module is used for protecting the talent quality data by using a differential privacy technology.
In this embodiment, the data collection module may collect talent quality data from multiple channels (e.g., resumes, interview records, performance data, etc.), so that the system may obtain more dimensional information. Such integrated data facilitates a more comprehensive assessment of the person being evaluated. The data integration and processing module uses natural language processing techniques to clean, entity identify, and emotion analyze data. This can help the system to better understand the expression and emotional tendency of the person being evaluated. The assessment module adopts deep learning and self-adaptive algorithm to construct an assessment model, so that the quality and the capability of the person to be assessed can be predicted more accurately. This helps to improve the accuracy of the recruitment decisions, thereby saving time and resources. The assessment module generates personalized assessment reports and suggestions that help the assessed individuals understand their strengths and areas of improvement. This personalized feedback helps the person being evaluated to improve his professional quality and increase the chance of success. The privacy protection module uses a differential privacy technology to protect talent quality data and ensure that sensitive information is fully protected. This helps ensure that the system complies with privacy regulations, maintains data security and establishes trust. The data storage module is responsible for storing various data in the system, and ensures the safety and usability of the data. This makes data management more efficient, helping to track and review data.
The above embodiments are merely illustrative of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention, and various modifications and improvements made by those skilled in the art to the technical solution of the present invention should fall within the scope of protection defined by the claims of the present invention without departing from the spirit of the design of the present invention.
Claims (8)
1. The talent element ability assessment method based on AI is characterized by comprising the following steps:
formulating clear evaluation indexes, wherein the evaluation indexes comprise evaluation technical capability, communication capability, leading potential and innovative thinking;
collecting talent quality data of a plurality of channels, and storing the talent quality data into a safe database;
integrating and processing the talent quality data through natural language processing;
creating labels for different quality and capacities, and extracting key features from the processed talent quality data;
based on the extraction and evaluation indexes of the features, constructing an evaluation model by using a deep learning and self-adaptive algorithm;
using the established evaluation model to evaluate the evaluated personnel, generating a corresponding evaluation report, and generating personalized suggestions according to the evaluation report;
performing data protection on talent quality data by using a differential privacy technology;
the method comprises the following steps of:
defining a target and an evaluation index of an evaluation model;
collecting and preprocessing talent quality data, and performing data cleaning, missing value filling and standardization treatment;
designing a neural network model structure, and defining a network layer, an activation function and an output layer;
training the neural network model by using the marked talent quality data;
using a cross entropy loss function and an optimization algorithm to guide a model training process and training a neural network model by using the marking data;
adjusting parameters and structures of the model by using an adaptive algorithm;
dividing the data into a training set and a verification set, and considering the weights and the relevance of different evaluation indexes;
the performance of the model is evaluated using the validation set.
2. The AI-based talent quality assessment method of claim 1, wherein said talent quality data comprises resume, interview record, performance data, educational background, and skill record.
3. The AI-based talent ability assessment method according to claim 1, wherein said integrating and processing of said talent quality data by natural language processing comprises the steps of:
acquiring talent quality data;
cleaning and processing the collected talent quality data;
determining entities in the text using natural language processing techniques;
carrying out emotion analysis on the text data;
classifying the text data according to different evaluation indexes;
the extracted features and classification information are integrated into one data set.
4. The AI-based talent ability assessment method of claim 1, wherein the adaptive algorithm comprises the steps of:
constructing an initial neural network model;
starting an iteration process of the self-adaptive algorithm, and analyzing talent quality data;
according to the analysis result of the data, the parameters of the model are adjusted;
dynamically adjusting the learning rate according to the progress of the training using a learning rate adjustment formula;
introducing regularization terms into the loss function of the model;
gradient clipping is used to control the magnitude of the gradient;
after each adaptation iteration, the performance of the model on the validation set is evaluated.
5. The AI-based talent ability assessment method of claim 4, wherein the learning rate adjustment formula is specifically:
，
wherein the initial learning rate is an initial value, k is an adjustment parameter, and epoch represents the number of iterative rounds of training.
6. The AI-based talent ability assessment method according to claim 1, wherein the data protection of talent quality data using differential privacy technology comprises the steps of:
acquiring talent quality data;
anonymizing talent quality data;
determining parameters of differential privacy;
when a user queries data, random noise is added to a query result, and the noise generation mode comprises Laplacian noise or Gaussian noise;
issuing a query result after privacy protection treatment;
and adjusting the differential privacy parameters according to the result of the privacy analysis to achieve proper balance.
7. The AI-based talent ability assessment method of claim 6, wherein the parameters of differential privacy are specifically epsilon and delta;
epsilon represents privacy parameters of differential privacy, the level of privacy protection is controlled, smaller epsilon provides stronger privacy protection, and epsilon is a positive number;
delta represents the probability of failure in a query for controlling the differential privacy algorithm, delta is typically very close to zero and can be considered a small value.
8. An AI-based talent ability assessment system, wherein the system is applied to the AI-based talent ability assessment method according to any one of claims 1 to 7, and comprises an index formulation module, a data collection module, a data integration and processing module, a label creation module, an evaluation module, a data storage module and a privacy protection module; the data storage module is in communication connection with the index making module, the data collecting module, the data integrating and processing module, the label creating module, the evaluating module and the privacy protecting module;
the index making module is used for making clear evaluation indexes, wherein the evaluation indexes comprise evaluation technical capability, communication capability, leading potential and innovation thinking;
the data collection module is used for collecting talent quality data from a plurality of channels and storing the data into a safe database;
the data integration and processing module is used for cleaning, entity identification and emotion analysis of the collected talent quality data by using a natural language processing technology, and classifying the data according to different evaluation indexes;
the label creation module is used for creating labels for different qualities and capacities and extracting key characteristics of the processed talent quality data;
the evaluation module is used for constructing a talent element ability evaluation model by using a deep learning and self-adaptive algorithm, wherein the self-adaptive algorithm comprises the steps of neural network construction, parameter adjustment, learning rate adjustment, regularization and gradient cutting, the established evaluation model is used for evaluating the personnel to be evaluated, a corresponding evaluation report is generated, and personalized advice is generated according to the evaluation report;
the data storage module is used for storing various data in the system;
the privacy protection module is used for protecting the talent quality data by using a differential privacy technology.
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