Human resource management system

Abstract

The invention provides a human resource management system. The human resource management system comprises a recruitment module, an intelligent training module, a performance evaluation module, an incentive mechanism module and a human planning module, the recruitment module collects current post demand information and current candidate feature data, performs intelligent matching and outputs a matching sorting result; the intelligent training module analyzes the employee learning data and adjusts training content in combination with a preset training target; the performance evaluation module records the performance data in a distributed account book; the incentive mechanism module is used for designing a work task containing a gamification element, calculating a gamification incentive index and updating the state of an employee based on the condition that the employee completes the work task; the human planning module collects historical multi-dimensional data to train a human demand prediction model so as to obtain a human demand prediction result and generate personnel configuration and recruitment plan suggestions, so that the recruitment matching precision is improved, training personalized dynamic adjustment is realized, the performance data is ensured to be real and reliable, the employee incentive effect is enhanced, and the human planning scientificity is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of human resource management, and in particular to a human resource management system. BACKGROUND

[0002] Traditional human resource management faces many technical problems: in the recruitment link, manual screening of resumes is inefficient and difficult to accurately match job requirements and candidate abilities; the training system lacks personalized design and cannot be dynamically adjusted according to the actual ability of employees; performance evaluation data is easy to tamper with, affecting the fairness of evaluation; the incentive mechanism is single and difficult to continuously stimulate the enthusiasm of employees; human planning lacks data support, resulting in unreasonable personnel allocation. These problems seriously restrict the efficiency and effectiveness of enterprise human resource management.

[0003] In the prior art, the recruitment system relies on keyword matching and cannot deeply analyze the correlation between job requirements and candidate characteristics; the training system adopts a fixed course mode and ignores individual differences among employees; the performance management system has data security risks; the incentive mechanism lacks innovative design; and human planning mainly relies on experience judgment and lacks scientific prediction methods. These technical defects make it difficult for human resource management to meet the development needs of modern enterprises. In view of the above problems, the prior art needs to be improved. SUMMARY

[0004] The present application provides a human resource management system to improve recruitment matching accuracy, achieve personalized dynamic adjustment of training, ensure the authenticity and reliability of performance data, enhance employee incentive effect, and improve the scientific nature of human planning.

[0005] In one aspect, the present application provides a human resource management system, comprising:

[0006] a recruitment module for collecting current job requirement information and current candidate characteristic data, extracting key features using natural language processing technology, and intelligently matching the current job requirement information and the current candidate characteristic data through a machine learning algorithm to output a matching ranking result of target candidates;

[0007] an intelligent training module for dynamically adjusting training content by analyzing employee learning data and combining preset training targets;

[0008] a performance evaluation module for recording performance data in a distributed ledger;

[0009] an incentive mechanism module for designing work tasks containing gamification elements, calculating gamification incentive indicators based on the completion of work tasks by employees, and updating employee status;

[0010] The human planning module is configured to collect historical multidimensional data, perform data cleaning, standardization and feature extraction, train a human demand prediction model by using a machine learning algorithm, input current multidimensional data, output a human demand prediction result, and generate personnel allocation and recruitment plan suggestions according to the human demand prediction result.

[0011] According to the human resource management system provided by the application, the recruitment module comprises:

[0012] The data collection unit is configured to collect current job demand information and current candidate characteristic data; wherein the current job demand information comprises job responsibilities, skill requirements, experience requirements and job priority, and the current candidate characteristic data comprises resume data, work experience and skill evaluation results;

[0013] The matching model construction unit is configured to use a machine learning algorithm to process the collected historical job demand information and historical candidate characteristic data to establish an associated matching model between jobs and candidates;

[0014] The candidate screening unit is configured to screen target candidates and generate a matching degree score of the target candidates according to the current job demand information and the current candidate characteristic data through the associated matching model, and output a matching ranking result of the target candidates.

[0015] According to the human resource management system provided by the application, the recruitment module further comprises:

[0016] The virtual scene construction unit is configured to customize a virtual work scene according to a core work scene of the current job demand information; wherein the virtual work scene contains a task script and an auxiliary tool entry corresponding to the current job demand information, and the virtual work scene sets a dynamic difficulty level and adjusts the scene difficulty according to the initial performance of the target candidate;

[0017] The AI role interaction unit is configured to configure an AI control role matched with the virtual work scene, wherein the AI control role has a preset identity attribute, an emotion trigger rule and a branch dialogue library, and dynamically outputs a text response, an emotion symbol and a reply speed according to the response of the target candidate;

[0018] The interactive process management unit is configured to record whole-process data when the target candidate participates in the virtual work scene interaction; wherein the whole-process data comprises time dimension data, behavior dimension data and language dimension data;

[0019] The behavior data analysis unit is configured to perform multidimensional analysis on the whole-process data, and convert abstract soft skills into quantifiable behavior index scores; wherein the behavior index comprises communication ability, adaptability, initiative and cultural fit, and the weights of the behavior indexes are dynamically allocated according to the job priority;

[0020] The evaluation report generation unit is configured to generate a comprehensive evaluation report based on the behavior index score, and the comprehensive evaluation report comprises scene basic information, soft skill evaluation results, cultural fit evaluation results, and recruitment decision suggestions.

[0021] According to the human resource management system provided by the application, the behavior data analysis unit is specifically used for:

[0022] The full-process data is preprocessed to extract multi-dimensional features related to abstract soft skill evaluation;

[0023] Based on the preset behavior index classification system, a quantitative evaluation model of communication ability, adaptability, initiative and cultural fit is constructed respectively; wherein the communication ability is scored by analyzing the language fluency, response relevance, question depth and listening performance of the target candidate in the interaction; the adaptability is scored by analyzing the coping strategy, adjustment speed and problem solving effect of the target candidate when facing sudden task script changes, AI role emotional changes or auxiliary tool failures; the initiative is scored by analyzing the frequency of initiating a dialogue or a task, the quantity and quality of improvement suggestions, and the degree of exploration of unknown scenarios; the cultural fit is scored by analyzing the matching degree of the behavior mode, language style and decision preference of the target candidate with the preset cultural dimensions of the enterprise;

[0024] According to the post priority in the current post demand information, the weight of each behavior index is dynamically allocated;

[0025] The weighted scores of each behavior index are fused to generate a comprehensive behavior evaluation score of the target candidate.

[0026] According to the human resource management system provided by the application, the intelligent training module comprises:

[0027] The learning evaluation unit is configured to evaluate the current ability level and learning progress of the employee through test and performance data analysis;

[0028] The content generation unit is configured to generate a training course suitable for the personal characteristics of the employee according to the current ability level, learning progress and preset training target;

[0029] The dynamic adjustment unit is configured to adjust the difficulty and content of the subsequent course in real time according to the progress and mastery degree in the learning process of the employee.

[0030] According to the human resource management system provided by the application, the intelligent training module further comprises:

[0031] The tacit knowledge evaluation unit is configured to:

[0032] Collecting employee interaction data from enterprise collaboration platform, knowledge management system and instant messaging tool;

[0033] Building a knowledge flow network based on the employee interaction data; wherein the nodes in the knowledge flow network represent employees, the node attributes include employee basic information and explicit skill labels, the network edges represent knowledge exchange relationships, and the edge attributes include direction, weight and knowledge field labels, and the weight is calculated based on exchange depth score, monthly exchange frequency and knowledge field correlation;

[0034] Calculating the multi-dimensional centrality scores of each employee in each knowledge field by using social network analysis algorithm, and calculating the comprehensive centrality score after standardizing the multi-dimensional centrality scores; wherein the multi-dimensional centrality includes degree centrality, betweenness centrality and closeness centrality;

[0035] Combining the comprehensive centrality score and the artificial evaluation result, dividing the tacit knowledge level of the employee in each knowledge field into multiple levels, and identifying special tacit knowledge roles;

[0036] Fusing the explicit skill data of the employee and the tacit knowledge evaluation result to build a complete knowledge and skill map including field layer, skill layer and level layer, the map is labeled with knowledge gap information and corresponding post requirement level, and can provide basis for training content generation.

[0037] According to the human resource management system provided by the application, the calculation method of the degree centrality of the employee in the i-th knowledge field is: calculating the first ratio of the number of knowledge output edges of the employee and the total number of employees in the i-th knowledge field minus one, and calculating the product of the first ratio and 100 to obtain the degree centrality of the employee in the i-th knowledge field;

[0038] The calculation method of the betweenness centrality of the employee in the i-th knowledge field is: calculating the second ratio of the number of shortest knowledge paths participated by the employee and the number of all shortest paths in the i-th knowledge field, and calculating the product of the second ratio and 100 to obtain the betweenness centrality of the employee in the i-th knowledge field;

[0039] The calculation method of the closeness centrality of the employee in the i-th knowledge field is: calculating the third ratio of the sum of the shortest path lengths from the employee to all other employees and the total number of employees in the i-th knowledge field minus one, and processing to a preset score range by standardization to obtain the closeness centrality of the employee in the i-th knowledge field;

[0040] The calculation method of the comprehensive centrality score of the employee in the i-th knowledge field is: multiplying the degree centrality of the employee in the i-th knowledge field, the betweenness centrality of the employee in the i-th knowledge field and the closeness centrality of the employee in the i-th knowledge field by the corresponding weight coefficient respectively and summing up.

[0041] According to the human resource management system provided by the application, the tacit knowledge evaluation unit is further used for:

[0042] According to the input employee identifier, the explicit skill gap and the tacit skill gap of the employee corresponding to the employee identifier are displayed, and training content is recommended based on the explicit skill gap and the tacit skill gap; wherein the training content includes matching the higher-level tacit knowledge employee in the ith knowledge field as a tutor for case teaching, or organizing external experts to share training activities combined with internal experience extraction.

[0043] According to the human resource management system provided by the application, the performance evaluation module comprises:

[0044] The data recording unit is used to write the performance data into the blockchain distributed ledger when the project is completed or the performance target is achieved;

[0045] The data storage unit is used to store the performance data in an unalterable manner;

[0046] The report generation unit is used to summarize the performance data of a specified period and generate a performance report.

[0047] According to the human resource management system provided by the application, the incentive mechanism module comprises:

[0048] The game element setting unit is used to define the task completion standard and the reward rule, set the integral acquisition mechanism and the grade promotion condition, design the badge type corresponding to the special achievement, and create the individual challenge task and the team cooperation task;

[0049] The progress tracking unit is used to track the task completion of the employee, accumulate the integral and the badge number, and adjust the employee grade and the ranking;

[0050] The reward management unit is used to issue the incentive feedback and the reward.

[0051] The human resource management system provided by the application realizes intelligent matching of candidates through the recruitment module, dynamically adjusts the training content through the intelligent training module, guarantees the data authenticity through the performance evaluation module, enhances the employee enthusiasm through the incentive mechanism module, and improves the prediction scientificity through the human resource planning module, so that the recruitment matching precision can be improved, the training individualization dynamic adjustment can be realized, the performance data authenticity can be guaranteed, the employee incentive effect can be enhanced, and the human resource planning scientificity can be improved. BRIEF DESCRIPTION OF DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings described below are some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0053] Figure 1 is a structural schematic diagram of the human resource management system provided by the embodiment of the present application. DETAILED DESCRIPTION

[0054] In order to make the objects, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be described clearly and completely below in combination with the drawings in the present application. Obviously, the described embodiments are some embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0055] In the prior art, the traditional human resource management method has many technical defects. The recruitment link relies on manual screening of resumes and subjective judgment, resulting in low matching efficiency and insufficient accuracy. The training system adopts a unified course setting, which cannot adapt to the learning ability and progress differences of different employees. The performance record system uses a centralized database for storage, which has the risk of data tampering and insufficient transparency. The incentive mechanism mainly adopts a fixed reward mode, which is difficult to continuously stimulate the enthusiasm of employees. Human planning mainly relies on experience judgment, and lacks a data-driven scientific prediction method. These technical defects lead to problems such as long recruitment cycle, poor training effect, low credibility of performance evaluation, insufficient employee motivation, and unreasonable human allocation for enterprises.

[0056] In order to solve the above problems, through analysis, it is found that the fundamental defect of the traditional method is the insufficient data processing ability and the single technical means. For the recruitment matching problem, think about how to use natural language processing technology to realize accurate extraction of job requirements and candidate characteristics. To solve the problem of individualized training, a dynamic adjustment algorithm is proposed to adapt to individual learning differences. In terms of performance management, explore the tamper-proof characteristics of blockchain technology to ensure data reliability. For the incentive mechanism, study the promoting effect of game elements on behavior motivation. In the human planning link, develop a multi-dimensional prediction model based on machine learning. By integrating these technical means, a systematic solution is formed.

[0057] Therefore, the present application proposes the following technical solutions:

[0058] Figure 1 is a structural schematic diagram of the human resource management system provided by the embodiment of the present application. As Figure 1As shown, the human resource management system of the present embodiment can include a recruitment module 11, an intelligent training module 12, a performance evaluation module 13, an incentive mechanism module 14, and a human planning module 15. The recruitment module 11 is configured to collect current job requirement information and current candidate characteristic data, extract key features using natural language processing technology, and perform intelligent matching of the current job requirement information and the current candidate characteristic data through a machine learning algorithm, and output a matching ranking result of target candidates.

[0059] The intelligent training module 12 is configured to dynamically adjust training content by analyzing employee learning data and combining preset training targets.

[0060] The performance evaluation module 13 is configured to record performance data in a distributed ledger to ensure the authenticity and reliability of the data.

[0061] The incentive mechanism module 14 is configured to design work tasks containing gamification elements, calculate gamification incentive indicators based on the completion of work tasks by employees, and update employee status.

[0062] The human planning module 15 is configured to collect historical multi-dimensional data, perform data cleaning, standardization, and feature extraction, train a human demand prediction model using a machine learning algorithm, input current multi-dimensional data, output a human demand prediction result, and generate personnel configuration and recruitment plan suggestions based on the human demand prediction result.

[0063] In a specific implementation process, the natural language processing technology refers to processing unstructured text data through semantic analysis and keyword extraction algorithms, which can be implemented using a BERT model or an LSTM neural network, and is used to extract standardized features from job descriptions and resumes. The machine learning algorithm refers to training a prediction model through supervised learning, which can be implemented using a random forest or a gradient boosting decision tree, and is used to establish the association between job requirements and candidate characteristics. The blockchain distributed ledger refers to a database system using a decentralized storage architecture, which can be implemented using the Hyperledger Fabric framework to ensure the non-tamperability and traceability of data storage. The gamification element refers to integrating task challenges, point systems, and virtual reward mechanisms into the workflow, which can be implemented using an achievement unlocking algorithm and a dynamic point calculation model to improve employee engagement. Multi-dimensional data includes time series business data and market environment variables. Multi-dimensional data can be integrated and cleaned using data warehouse technology to provide high-quality input for the prediction model. For example, historical multi-dimensional data can include historical human demand data, historical business growth data, historical market trends, historical economic indicators, and historical technology development trends. Current multi-dimensional data can include current human demand data, current business growth data, current market trends, current economic indicators, and current technology development trends.

[0064] Specifically, the recruitment module 11 collects job requirement texts and candidate resume data, extracts standardized feature vectors through natural language processing, inputs a pre-trained machine learning model for similarity calculation, and outputs a matching degree ranking result. The intelligent training module 12 continuously monitors the learning behavior data and test scores of employees, dynamically adjusts the content difficulty and teaching methods of subsequent courses through analysis of knowledge mastery curves. The performance evaluation module 13 automatically captures key performance indicators at the completion of the project, writes them into the blockchain node through the smart contract to form a permanent record. The incentive mechanism module 14 sets game rules for each work task, updates the score level in real time according to the completion and triggers the reward mechanism. The human planning module 15 integrates historical business data and external environmental variables, predicts future human resource demand gaps through machine learning models, and automatically generates configuration schemes including recruitment size and skill requirements.

[0065] The present application uses a deep learning model to realize semantic-level feature matching, dynamically adjusts the algorithm to realize personalized teaching paths, introduces blockchain technology to ensure data credibility, enhances behavior driving force through gamification mechanism, and upgrades experience-based human planning to a data-driven prediction model, significantly improving the scientific nature of decision-making.

[0066] Through the above technical solutions, the present application effectively solves a number of technical problems in human resource management. It realizes accurate matching of job requirements and candidate features, improves recruitment efficiency and quality. Through dynamic adjustment of personalized training content, the skill training effect of employees is improved. The use of blockchain technology ensures the authenticity and reliability of performance data, providing a reliable basis for objective evaluation. The game-based incentive mechanism enhances employee participation and task completion rate. The machine learning-based human resource demand prediction model helps enterprises to make talent reserve planning in advance and optimize the efficiency of human resource allocation.

[0067] In some embodiments, the present application further proposes a specific implementation of the recruitment module 11, including a data collection unit, a matching model construction unit and a candidate screening unit. The data collection unit is used to collect current job requirement information and current candidate feature data, the current job requirement information including job responsibilities, skill requirements, experience requirements and job priority, and the current candidate feature data including resume data, work experience and skill evaluation results. The matching model construction unit is used to process the collected historical job requirement information and historical candidate feature data using machine learning algorithms to establish an associated matching model between jobs and candidates. The candidate screening unit is used to screen target candidates according to the current job requirement information and the current candidate feature data through the associated matching model and generate a matching degree score, and output a matching ranking result.

[0068] The data collection unit refers to a data acquisition module for obtaining structured job requirements and candidate characteristics, which can be implemented by connecting enterprise recruitment systems and third-party recruitment platforms through API interfaces, and analyzing unstructured text data through natural language processing technology. This unit provides uniform input for subsequent model processing through standardized data formats.

[0069] The matching model construction unit refers to a machine learning module for training the person-job matching relationship based on historical data, which can be implemented by combining random forest algorithm with feature engineering method, and a prediction model is established by analyzing the association mode of job and candidate characteristics in historical successful cases. This unit replaces traditional manual experience judgment through data-driven modeling.

[0070] The candidate screening unit refers to a decision module for real-time screening by applying the matching model, which can be implemented by using a distributed computing framework to process large-scale candidate data, and a quantitative score is generated through the probability value output by the model. This unit improves processing efficiency through an automated screening mechanism.

[0071] Specifically, when a new job requirement is generated by an enterprise, the data collection unit first extracts the job responsibilities, skill requirements and other structured information from the internal system, and obtains the resumes and evaluation reports of candidates from the recruitment platform. The matching model construction unit calculates the similarity between the current job requirement feature vector and the candidate feature vector based on the model trained based on historical recruitment data. The candidate screening unit sorts all candidates according to the matching score output by the model, and generates a recommended list containing the top N candidates. For example, when screening candidates for a Java development position, the model will focus on matching programming language proficiency, project experience years and other core features, automatically filter out candidates who do not meet the hard conditions, and screen out target candidates, generate matching score of target candidates, and output matching sorting result of target candidates.

[0072] The present application can capture non-linear matching rules by establishing multi-dimensional feature association through machine learning models, such as identifying the value of cross-industry experience or the transferability of potential skills. Further, the model can be retrained with new data regularly to continuously optimize matching accuracy.

[0073] Through the above technical solutions, the present application realizes intelligent matching of job requirements and candidate characteristics, and solves the technical problems of low efficiency and large subjective bias in traditional recruitment process. The specific advantages are the ability to automatically process massive candidate data, the objective standard for quantitatively evaluating candidate adaptability, and the technical advantage of dynamically updating the matching model to maintain evaluation accuracy. For example, when processing complex job requirements containing multiple dimensions, the system can complete precise screening of a thousand-level candidate database within milliseconds.

[0074] In some embodiments, the present application further proposes that the recruitment module 11 further comprises a virtual scene building unit, an AI role interaction unit, an interaction process management unit, a behavior data analysis unit and an evaluation report generation unit. The virtual scene building unit customizes a virtual working scene according to the core working scene of the current job requirement information, the virtual working scene contains a task script and an auxiliary tool entry corresponding to the job requirement, and a dynamic difficulty level is set to adjust the scene difficulty according to the initial performance of the target candidate. The AI role interaction unit configures an AI control role matched with the virtual working scene, the role has preset identity attributes, emotion trigger rules and branch dialogue library, and can dynamically output text responses, emotion symbols and reply speeds according to the responses of the target candidate. The interaction process management unit records the whole process data of the target candidate participating in the virtual working scene, including time dimension data, behavior dimension data and language dimension data. The behavior data analysis unit performs multi-dimensional analysis on the whole process data, converts abstract soft skills into quantifiable behavior index scores, and the weights of each behavior index are dynamically allocated according to the priority of the post. The evaluation report generation unit generates a comprehensive evaluation report containing scene basic information, soft skill evaluation results, cultural fit evaluation results and recruitment decision suggestions based on the behavior index scores.

[0075] Among them, the virtual working scene refers to a digital working environment constructed by three-dimensional modeling technology, which can be realized by Unity engine or UnrealEngine, and its task script contains task sequence and challenge setting in typical working scene, which is used to simulate typical situation in actual working scene. The AI control role refers to a virtual interactive object constructed based on natural language processing technology, which can be realized by GPT model combined with emotion calculation algorithm, and its branch dialogue library contains preset dialogue path and response logic, which is used to simulate interpersonal interaction in real working scene. The whole process data acquisition refers to recording the operation track of the user in the virtual scene by burying point technology, which can be realized by timestamp recording, behavior log collection and speech to text technology, which is used to completely restore the interaction process of the candidate. Multi-dimensional analysis refers to feature extraction and pattern recognition on structured data, which can be realized by machine learning classification algorithm combined with weighted scoring model, which is used to convert unstructured behavior data into quantifiable evaluation index.

[0076] Specifically, when a recruitment demand arises, the virtual scene construction unit first parses the core work elements in the job requirement, such as extracting cross-department collaboration, risk response, and other key scene elements for a project management position, and generates a virtual scene containing tasks such as sudden task changes and stakeholder communication. The AI character interaction unit deploys virtual colleague characters with different department attributes in the scene and sets their emotional trigger rules to automatically trigger a questioning emotion when the candidate proposes an unreasonable solution. The candidate interacts with the AI character in real time through voice or text, and the system records the full-process interaction data from task reception to final report. The behavior data analysis unit extracts features such as the candidate's strategy adjustment speed when responding to sudden changes and the frequency of initiating communication, and calculates scores for communication ability, adaptability, and other indicators through a pre-set quantitative model. For innovative positions with high priority, the initiative indicator weight coefficient is increased. The final evaluation report not only contains the scores, but also marks the matching degree of the candidate's decision-making preference to the corporate culture.

[0077] The present application can effectively simulate complex interaction scenarios in a real work environment by constructing a dynamically adjustable virtual work scene and combining AI characters with emotional feedback, making the candidate's behavior more realistic and reliable for soft skill evaluation.

[0078] Through the above technical solutions, the present application can convert the soft skill elements that are difficult to quantify in traditional recruitment into comparable quantitative indicators, solving the problem of strong subjectivity in evaluating core qualities such as communication ability and adaptability in the prior art. By dynamically adjusting the scene difficulty and indicator weight, the evaluation standard is highly adaptable to the actual job requirements, avoiding evaluation bias caused by using a unified standard. Based on multi-dimensional analysis of full-process behavior data, potential risk points in the candidate's behavior pattern can be identified, such as decision-making hesitation or communication barriers in high-pressure scenarios, providing a more comprehensive reference for recruitment decisions.

[0079] In some embodiments, the behavior data analysis unit is further configured to: preprocess the full-process data, including data cleaning, denoising, and structuring, and extract multi-dimensional features related to abstract soft skill evaluation; build quantitative evaluation models for communication ability, adaptability, initiative, and cultural fit based on a pre-set behavior indicator classification system; dynamically allocate weights to each behavior indicator according to the job priority in the current job requirement information; and fuse the weighted scores of each behavior indicator to generate a comprehensive behavior evaluation score for the target candidate.

[0080] The data cleaning refers to automatically identifying and correcting abnormal values, missing values and repeated values in the data by an algorithm, and can be specifically realized by adopting regular expression matching combined with artificial checking rules, and is used for ensuring the integrity and consistency of the input data. The denoising processing refers to filtering irrelevant interference information of the target, and can be specifically realized by adopting a noise recognition algorithm based on semantic analysis, and is used for improving the accuracy of feature extraction. The structured processing refers to converting non-standardized interactive data into a uniform format feature vector, and can be specifically realized by adopting a word vector conversion technology in natural language processing combined with a time sequence encoding method, and is used for supporting subsequent model operation.

[0081] Specifically, the original interactive data generated in the virtual scene interaction process is first subjected to the data cleaning module, automatically corrects the time stamp error, and supplements the missing behavior type label. The denoising module filters irrelevant chatting content through semantic analysis, and retains effective interactive records related to the work scene. The structured processing module converts the free text dialogue into a structured data set containing features such as word frequency, sentiment polarity and response delay. Based on the preset evaluation index system, four independent evaluation models are respectively constructed: the communication ability model analyzes the sentence coherence, topic relevance and question depth in the dialogue record; the adaptability model detects the strategy adjustment speed and problem solving effect when the task changes; the initiative model counts the scene exploration times and the number of autonomous suggestions; and the cultural fit model compares the matching degree of the behavior mode and the enterprise value database. After the standardized scores of each model are output, the priority configuration of the recruitment post is adjusted, for example, the technical post focuses on adaptability, and the management post focuses on communication ability, and the weight proportion of each index is automatically adjusted. Finally, a comprehensive evaluation score is generated through a weighted summation algorithm, providing a quantitative basis for recruitment decision-making.

[0082] The application realizes fine-grained quantitative analysis of abstract soft skills through a multi-model parallel evaluation architecture, and combines a dynamic weight distribution mechanism to make the evaluation results reflect the comprehensive ability of the candidate and accurately match the core needs of different posts.

[0083] Through the above technical solutions, the application realizes multi-dimensional objective evaluation of the soft skills of the candidate, solves the technical problems of large subjective judgment deviation and non-uniform evaluation standards in traditional recruitment. Through the dynamic weight adjustment mechanism, it ensures that the recruitment needs of different post types can be accurately matched, improves the scientificity of the person-post matching and the reliability of the recruitment decision-making. The structured data processing flow effectively improves the utilization rate of unstructured interactive information, so that the originally difficult-to-quantify behavior characteristics are converted into comparable evaluation indexes.

[0084] In some embodiments, the present application further proposes that the intelligent training module 12 comprises a learning evaluation unit, a content generation unit and a dynamic adjustment unit. The learning evaluation unit is used to evaluate the current ability level and learning progress of the employee through test and performance data analysis; the content generation unit is used to generate a training course suitable for the individual characteristics of the employee according to the current ability level, learning progress and preset training target; and the dynamic adjustment unit is used to adjust the difficulty and content of the subsequent course in real time according to the progress and mastery degree of the employee in the learning process.

[0085] Among them, the learning evaluation unit refers to a module for obtaining employee ability data through a standardized test tool and a performance data acquisition interface, which can be realized by connecting an online test platform and a performance management system, and a multi-dimensional ability evaluation model is established by analyzing the correctness distribution in the test results, the knowledge point mastery degree and the task completion quality in the performance data. This unit solves the problem of single evaluation method before traditional training and the inability to quantify the ability level.

[0086] Among them, the content generation unit refers to a module for automatically combining training materials based on the ability evaluation results, which can be realized by associating course knowledge points and ability requirements through knowledge graph technology, and a learning path is generated by selecting matching knowledge modules from a preset course library according to the gap between the current ability and the target ability of the employee. This unit overcomes the defect that traditional uniform courses cannot adapt to individual differences.

[0087] Among them, the dynamic adjustment unit refers to a module for real-time monitoring of the learning process and optimization of course parameters, which can be realized by combining learning behavior analysis algorithm and adaptive learning system, and the course difficulty coefficient and knowledge density are dynamically adjusted by tracking the course completion rate, the number of repeated learning of knowledge points and the accuracy rate change trend. This unit eliminates the disadvantage that fixed course progress cannot adapt to changes in learning rhythm.

[0088] Specifically, the learning evaluation unit first evaluates the skills of the employee through the online test platform, and extracts historical task completion data from the performance management system. The test platform can configure different difficulty question banks, such as setting basic operation type questions for junior employees and complex scene application questions for senior employees. After cleaning the performance data, key indicators such as task response speed, error rate and innovation score are extracted. The content generation unit compares the evaluation results with the skill standards required by the post, for example, when it is detected that the data analysis tool usage ability of an employee is not up to standard, a phased course including data cleaning, visualization analysis and modeling skills is automatically generated. The dynamic adjustment unit continuously monitors learning behavior during the implementation of the course, for example, when the system finds that the employee's correct rate for a specific knowledge point practice is consistently higher than the threshold, it automatically skips the redundant explanation module; when it is detected that the average learning time of a module exceeds the preset range, the course difficulty reduction mechanism is triggered.

[0089] The present application realizes precise matching of training content and individual ability curve by constructing an ability evaluation model through integrating multi-source data, and establishes a dynamic adjustment mechanism based on learning process data to keep course difficulty and learning progress synchronized and optimized.

[0090] Through the above technical solutions, the present application can generate individualized training programs according to the real-time ability state of employees, avoid resource waste caused by repeated training of mastered skills, and ensure that the learning content is always in the appropriate challenge interval through the dynamic adjustment mechanism, effectively maintaining learning enthusiasm. In the implementation process, the system can automatically identify the knowledge modules corresponding to the ability short board, such as preferentially arranging collaborative scenario simulation courses for employees with poor communication skills and strengthening tool practical training for employees with weak technical ability, thereby improving the efficiency of training resource allocation. When an employee makes significant progress in a specific field, the system can unlock advanced courses in advance, such as automatically adding machine learning application cases for employees who quickly master Python basics, to realize precise matching of training pace and individual development speed.

[0091] Since the traditional training system mainly relies on explicit skill testing and course completion evaluation, it cannot effectively identify the tacit knowledge accumulated by employees through practical experience. The knowledge management system in the prior art usually only records document materials and explicit knowledge assets, and lacks the ability to analyze the knowledge flow process and personnel interaction relationship. Therefore, the present application further proposes that the intelligent training module 12 further includes a tacit knowledge evaluation unit, the tacit knowledge evaluation unit is configured to collect employee interaction data from an enterprise internal collaboration platform, a knowledge management system, and an instant messaging tool; construct a knowledge flow network based on the employee interaction data; wherein the nodes in the knowledge flow network represent employees, the node attributes include employee basic information and explicit skill labels, the network edges represent knowledge exchange relationships, and the edge attributes include direction, weight, and knowledge field labels, the weight is calculated based on exchange depth score, monthly exchange frequency, and knowledge field relevance; calculate the multi-dimensional centrality score of each employee in each knowledge field using a social network analysis algorithm, and calculate the comprehensive centrality score after standardizing the multi-dimensional centrality score; wherein the multi-dimensional centrality includes degree centrality, betweenness centrality, and closeness centrality; combine the comprehensive centrality score and the artificial evaluation result to divide the tacit knowledge level of employees in each knowledge field into multiple levels and identify special tacit knowledge roles; integrate the explicit skill data of employees and the tacit knowledge evaluation result to construct a complete knowledge and skill map including a field layer, a skill layer, and a level layer, the map is labeled with knowledge gap information and corresponding job requirement level, and can provide a basis for training content generation.

[0092] The implicit knowledge evaluation unit refers to a module for identifying the level of implicit knowledge by analyzing the knowledge exchange behavior between employees, and can be implemented by using a social network analysis algorithm combined with an artificial evaluation mechanism, and is used to discover practical experience and skills that are difficult to capture by traditional training systems. The knowledge flow network refers to a structured model reflecting the knowledge dissemination path within an organization, and can be constructed by using a graph database technology, the node attributes include employee basic information and explicit skill tags, and the edge attributes record the field, frequency and depth of knowledge exchange, and are used to visually present the knowledge distribution and flow characteristics. The multi-dimensional center degree score includes three dimensions of degree center degree, betweenness center degree and closeness center degree, and the dimension scores can be unified in dimension by using a standardization formula, and then weighted summation is performed, and is used to comprehensively evaluate the influence and hub role of employees in the knowledge network. The knowledge skill map refers to a multi-layer knowledge structure model integrating explicit and implicit skills, and can be constructed by using a knowledge graph technology, and the gap between knowledge gaps and post requirements is labeled, so as to provide data support for personalized training.

[0093] Specifically, the implicit knowledge evaluation unit automatically collects the knowledge interaction data of employees in daily work by accessing the enterprise internal collaboration platform, the knowledge management system and the instant messaging tool. When the knowledge flow network is constructed based on these data, each employee is modeled as a network node, the node attribute records the basic information and the authenticated explicit skill, and the network edge represents the knowledge transmission relationship between employees, and the weight of the edge is quantitatively calculated by analyzing the professional depth of the exchange content, the exchange frequency and the relevance to the knowledge field. The social network analysis algorithm is used to calculate the degree center degree, the betweenness center degree and the closeness center degree of each employee in a specific knowledge field, wherein the degree center degree reflects the breadth of direct knowledge output of the employee, the betweenness center degree embodies the hub role of the employee in the knowledge dissemination path, and the closeness center degree represents the efficiency of the employee in obtaining knowledge. After the center degree scores of the three dimensions are standardized, the comprehensive center degree score is calculated according to the preset weight coefficient, the implicit knowledge level of the employee is classified by combining the artificial evaluation result, and the key knowledge disseminators and field experts are identified. Finally, the explicit skill data and the implicit knowledge evaluation result are fused to construct a three-dimensional knowledge skill map including the field layer, the skill layer and the level layer, and the map automatically identifies the knowledge gap and generates targeted training suggestions by comparing the current level of the employee with the post requirement level.

[0094] The present application can objectively quantify the actual contribution and influence of employees in the organization knowledge system by constructing the knowledge flow network and calculating the multi-dimensional center degree index, and solves the technical problem that the implicit knowledge is difficult to evaluate.

[0095] By the technical solution, the application can accurately identify the implicit knowledge level of the employee in a specific field, and provide more comprehensive ability evaluation basis for training content generation. By constructing the knowledge and skill map, the system can automatically match the knowledge gap and the training resource, for example, recommending knowledge sharing training for the employee with high implicit knowledge level, and arranging targeted skill improvement courses for the employee with knowledge gap. The evaluation method based on social network analysis effectively makes up for the deficiency of the traditional training system in evaluating practical experience, so that the generated training plan is more suitable for the actual ability development needs of the employee.

[0096] In some embodiments, the application further proposes a calculation method of the degree centrality of the employee in the i-th knowledge field, which is: calculating a first ratio value of the number of knowledge output edges of the employee and the total number of employees in the i-th knowledge field minus one, and calculating a product of the first ratio value and 100 to obtain the degree centrality of the employee in the i-th knowledge field; a calculation method of the betweenness centrality of the employee in the i-th knowledge field, which is: calculating a second ratio value of the number of shortest knowledge paths participated by the employee and the total number of shortest paths in the i-th knowledge field, and calculating a product of the second ratio value and 100 to obtain the betweenness centrality of the employee in the i-th knowledge field; a calculation method of the closeness centrality of the employee in the i-th knowledge field, which is: calculating a third ratio value of the sum of the shortest path lengths from the employee to all other employees and the total number of employees in the i-th knowledge field minus one, and normalizing to a preset score range to obtain the closeness centrality of the employee in the i-th knowledge field; and a calculation method of the comprehensive centrality score of the employee in the i-th knowledge field, which is: multiplying the degree centrality of the employee in the i-th knowledge field, the betweenness centrality of the employee in the i-th knowledge field, and the closeness centrality of the employee in the i-th knowledge field by the corresponding weight coefficients respectively and summing them up.

[0097] Among them, the degree centrality refers to the measure of the knowledge output activity of the employee in a specific knowledge field, which can be realized by calculating the proportion of the number of knowledge exchange relationships of the employee and the total number of employees in the field, and the index reflects the direct influence of the employee in the knowledge network; the betweenness centrality refers to the measure of the intermediary role of the employee in the knowledge dissemination path, which can be realized by calculating the proportion of the shortest knowledge path participated by the employee, and the index reflects the hub value of the employee in the cross-team knowledge flow; the closeness centrality refers to the measure of the efficiency of the employee in obtaining knowledge, which can be realized by calculating the average distance of the shortest path between the employee and other members, and the index reflects the information accessibility of the employee in the knowledge network; and the comprehensive centrality score refers to the evaluation value of the integrated multi-dimensional network characteristics, which can be realized by weighted summing different centrality indexes, and the index is used for comprehensive evaluation of the implicit knowledge contribution of the employee.

[0098] Specifically, the knowledge flow network is constructed by collecting internal collaboration data, the nodes represent employees, and the edges represent knowledge interaction relationships. When calculating the degree centrality, the proportion of the number of times an employee actively shares knowledge to the total number of exchanges in the field is counted. For example, in a field with 20 employees, if an employee outputs knowledge 15 times, the degree centrality is (15 / (20-1))x100=78.9. When calculating the betweenness centrality, all the shortest paths of knowledge transmission are identified, and the proportion of the paths in which an employee participates is counted. For example, in a field with 100 shortest paths, if an employee participates in 30 of them, the betweenness centrality is (30 / 100)x100=30. When calculating the closeness centrality, the average path length of an employee to other members is measured. For example, if the sum of the shortest paths of an employee to other members is 50, the standardized score is 65. The comprehensive score is obtained by combining the above indicators according to the preset weights. For example, the degree centrality weight is 0.4, the betweenness weight is 0.3, and the closeness weight is 0.3. The comprehensive score is 78.90.4+300.3+65*0.3=55.6. This calculation system can quantitatively identify the core contributors in the knowledge network.

[0099] The present application can more accurately identify employees with real tacit knowledge value by constructing a multi-dimensional network analysis model, considering knowledge output, information hub role and knowledge acquisition efficiency. For example, an employee may not have a high knowledge output, but frequently plays a bridge role in cross-department collaboration. Traditional evaluation may miss his value, but the present application can effectively capture such tacit contributions through betweenness centrality.

[0100] Through the above technical solutions, the present application can accurately quantify the multi-dimensional role of employees in the knowledge network, solve the problem of strong subjectivity and single dimension in tacit knowledge evaluation, and provide an objective basis for training content recommendation. For example, when identifying an employee with high betweenness centrality but insufficient explicit skills, the system can recommend communication and collaboration training. When finding an employee with high closeness centrality but insufficient knowledge output, the system can suggest participating in knowledge sharing activities, thereby improving the organization's knowledge management level.

[0101] In some embodiments, the present application further proposes that the tacit knowledge evaluation unit displays the corresponding explicit skill gap and tacit skill gap according to the input employee identifier, and recommends training content based on these gaps. The training content includes matching high-level tacit knowledge employees in a specific knowledge field to serve as mentors for case teaching, or organizing external experts to share training activities combined with internal experience extraction.

[0102] The explicit skill gap refers to the gap between the quantifiable skills through standardized tests or certification results and the requirements of the post, which can be realized by skill matrix comparison algorithm, by matching the skills certificate obtained by the employee with the skill list required by the post to calculate the matching degree. The implicit skill gap refers to the lack of experience-based knowledge or practical ability which is difficult to quantify by traditional evaluation methods, which can be realized by social network analysis algorithm, by analyzing the interaction mode of the employee in the knowledge flow network to identify the weak links of the implicit knowledge. The high-level implicit knowledge employee refers to the employee with outstanding implicit knowledge level in a specific knowledge field, which can be realized by setting a threshold for the comprehensive center degree score, for example, the top 10% of employees with the highest comprehensive center degree score are identified as high-level implicit knowledge holders in the field.

[0103] Specifically, when the system receives the employee identification input, first, the explicit skill data of the employee is extracted from the knowledge skill map, and the explicit skill gap report is generated by comparing the difference with the post requirements. At the same time, by analyzing the interaction data of the employee in the knowledge flow network, the comprehensive center degree score of the employee in each knowledge field is calculated, and compared with the preset post requirement level to identify the implicit skill gap. For the ith identified knowledge field gap, the system automatically retrieves the high-level employees with a comprehensive center degree score reaching a preset threshold in the field to generate case teaching matching suggestions. If internal resources are insufficient, an external expert training scheme is triggered, such as recommending a special seminar integrating external practical cases and internal best experience in the field. The system ensures the real-time and applicability of the recommended content by dynamically updating the tutor database and external resource library.

[0104] The present application can simultaneously find the deficiencies of employees in both structured knowledge and unstructured experience by fusing explicit skill matrix and implicit knowledge network analysis. For example, in a software development team, although an engineer passes the programming test, the actual code review participation is low. The system identifies the implicit gap in the code optimization experience transmission ability by analyzing the number of times the code submission is cited and the frequency of peer review interaction, and then recommends the engineer to participate in the cross-project code review meeting with an architect.

[0105] Through the above technical solutions, the present application solves the technical problem that the traditional training system cannot effectively identify and make up the implicit knowledge gap. The system can accurately identify the specific weak links of employees in knowledge application and experience transmission by combining explicit skill comparison and implicit network analysis. The teaching matching mechanism generated based on the double gap analysis results improves the transmission efficiency of experience-based knowledge, for example, a new employee can improve the problem solving efficiency to the average level of the team within 3 months by following a high-level employee to participate in actual case processing. At the same time, the integrated training design of external experts and internal experience effectively makes up the limitations of single source knowledge transmission.

[0106] In some embodiments, the present application further proposes that the performance evaluation module 13 comprises a data recording unit for writing performance data into the blockchain distributed ledger when a project is completed or a performance target is achieved; a data storage unit for storing performance data in an unalterable manner; the performance data includes order amount, completion time, project results and customer evaluation data, wherein the data recording unit records the data into the blockchain in real time when the data is generated, and the performance report supports cross-regional comparison and analysis; a report generation unit for summarizing performance data of a specified period and generating a performance report.

[0107] The blockchain distributed ledger refers to a distributed database system constructed based on blockchain technology, which can be implemented using a consortium chain architecture, and data consistency is ensured through a consensus mechanism. This feature ensures data unalterability through distributed storage and encryption algorithms, solving the problem of easy modification of traditional centralized storage. Real-time recording refers to immediate on-chain operation after data generation, which can be implemented using an event-driven data collection interface, and the recording process is automatically started through a pre-set trigger condition to ensure data timeliness. The cross-regional comparison and analysis function is realized through standardized data format and unified timestamp, and a multi-dimensional data index structure can be established to support data aggregation according to conditions such as region and time.

[0108] Specifically, when a project is completed or a performance target is achieved, the data recording unit automatically captures performance data through the interface with the enterprise business system. Structured data such as order amount and completion time is converted in format and packaged together with unstructured data such as customer evaluation to generate a data block. The data block is added to the blockchain network after being verified by the consensus node, forming a chain-like storage structure with a timestamp. The data storage unit uses the hash algorithm of the blockchain to encrypt each block, forming an anti-tamper data chain associated with the previous block. The report generation unit analyzes the time series data in the blockchain, extracts relevant fields for statistical analysis according to the pre-set period, and generates a performance report containing trend charts and comparative analysis.

[0109] In a specific implementation process, order amount data can be automatically obtained by associating with the financial module interface of the enterprise ERP system, and completion time data can be collected by triggering the workflow state change event of the project management system. Customer evaluation data can be integrated with the API interface of the online evaluation platform, and sentiment tendency scores can be extracted using natural language processing technology. The cross-regional comparison function automatically adds a regional identification code when data is chained by establishing a unified geographic location coding system, allowing the report generation unit to perform data grouping statistics according to regional dimensions.

[0110] The application greatly improves the difficulty of data tampering by obtaining the majority node consensus of data modification through the distributed storage characteristics of the blockchain. In the prior art, data collection often has a delay, and the application ensures that the data is timely chained through the real-time recording mechanism, avoiding the delay or omission of data caused by human intervention, and improving the integrity of data collection. The standardized data structure and cross-regional analysis function provide enterprise managers with a unified perspective of performance view, supporting decision-making based on objective data. The application of blockchain technology also enhances the anti-attack ability of the system, preventing data loss risks caused by single point failure.

[0111] Through the above technical solutions, the application realizes the whole-process traceability and tamper-proof storage of performance data, ensuring the authenticity and reliability of the evaluation basis. The real-time recording mechanism avoids data delay or omission caused by human operation, improving the integrity of data collection. The standardized data structure and cross-regional analysis function provide enterprise managers with a unified perspective of performance view, supporting decision-making based on objective data. The application of blockchain technology also enhances the anti-attack ability of the system, preventing data loss risks caused by single point failure.

[0112] In some embodiments, the application further proposes an incentive mechanism module 14, which includes a game element setting unit for defining task completion standards and reward rules, setting an integral acquisition mechanism and a level promotion condition, designing a badge type corresponding to a special achievement, and creating individual challenge tasks and team collaboration tasks; a progress tracking unit for tracking employee task completion, accumulating points and badge numbers, and adjusting employee levels and rankings; and a reward management unit for issuing incentive feedback and rewards.

[0113] The integral acquisition mechanism refers to converting the degree of task completion into quantifiable numerical points, which can be realized by using a preset integral calculation rule, for example, setting a basic integral value according to the task complexity and giving additional points based on the completion quality, thereby establishing an objective quantitative evaluation standard. The level promotion condition refers to the rule that triggers job level promotion when an employee reaches a certain integral threshold, which can be realized by using a ladder threshold setting method, for example, setting bronze, silver, gold and other level intervals, each interval corresponding to different permissions and rewards, thereby forming a visual growth path. The badge type refers to a virtual identifier representing a specific achievement, which can be realized by using graphical design combined with achievement triggering conditions, for example, setting a "High Efficiency Star" badge that needs to be completed for three consecutive days, thereby increasing the richness of the achievement system. The individual challenge task refers to a dedicated task designed for individual ability improvement, which can be realized by using an adaptive algorithm to generate differentiated challenge content, for example, dynamically adjusting the task difficulty based on the employee's historical performance to ensure the balance between challenge and accessibility. The team collaboration task refers to a composite task that requires the cooperation of multiple people, which can be realized by using a cross-department task allocation and progress coordination mechanism, for example, setting a team integral pool and a shared reward mechanism to promote cross-team collaboration efficiency.

[0114] Specifically, the game element setting unit first defines the task evaluation dimension and the integral conversion rule, and converts the work results into standardized numerical values. When the employees complete tasks such as document writing, customer service or project promotion, the system automatically calculates the integral value according to the preset algorithm and updates the personal account. The progress tracking unit monitors the completion status of each task in real time, synchronizes the integral change through the data interface, automatically triggers the grade promotion process when the cumulative integral reaches the promotion threshold, and records the achievement conditions of special achievements and issues corresponding badges. The reward management unit automatically matches the preset reward strategy according to the real-time grade and badge data, such as issuing virtual item exchange rights or applying for physical reward qualifications. For team collaboration tasks, the system establishes a cross-department task board to display the contribution values of each member in real time, and distributes team integrals to individual accounts according to the preset proportion after the task is completed.

[0115] The present application realizes real-time quantification and dynamic adjustment of incentive indicators by combining gamification elements with an automated tracking system, solving the problem of disconnection between incentive measures and work performance in traditional methods. Further, by setting up a multi-dimensional achievement system and a team collaboration mechanism, the limitations of single material rewards are broken through, and a more attractive composite incentive model is constructed.

[0116] Through the above technical solutions, the present application realizes real-time linkage of work performance and incentive feedback, and improves the enthusiasm and persistence of employees in participating in tasks. The systematic integral grade system provides a clear growth path for employees, and the diversified badge system enhances the sense of achievement. The introduction of team collaboration tasks promotes the integration of cross-department resources and optimizes the execution efficiency of complex tasks. The automated processing mechanism reduces the need for manual intervention, ensuring the objectivity and timeliness of incentive evaluation, effectively solving the technical defects of traditional incentive mechanisms such as slow response and ambiguous standards.

[0117] It should be noted that the related information involved in each embodiment of the present application is strictly in accordance with the requirements of laws and regulations, and follows the principles of legality, legitimacy and necessity, based on the reasonable purpose of business scenarios, processes the information provided by the user in the process of using the product / service or generated due to the use of the product / service, and the information authorized by the user.

[0118] The related information handled by the present application may vary depending on the specific product / service scenario, and should be based on the specific scenario of the user using the product / service. It may involve the user's account information, device information or other related information. The present application will treat the related information and its processing with a high degree of diligence and obligation.

[0119] The present application attaches great importance to the security of related information, and has taken security protection measures in accordance with industry standards, which are reasonable and feasible to protect related information from unauthorized access, public disclosure, use, modification, damage or loss.

[0120] The device embodiments described above are only illustrative, wherein the units described as separate components can or can not be physically separated, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed to multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment scheme according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0121] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A human resource management system, characterized by, The application comprises: a recruitment module for collecting current job requirement information and current candidate characteristic data, extracting key features using natural language processing technology, and intelligently matching the current job requirement information with the current candidate characteristic data through a machine learning algorithm to output a matching ranking result of target candidates; an intelligent training module for dynamically adjusting training content by analyzing employee learning data and combining preset training targets; a performance evaluation module for recording performance data in a distributed ledger; an incentive mechanism module for designing work tasks containing gamification elements, calculating gamification incentive indicators based on the completion of work tasks by employees, and updating employee status; a human planning module for collecting historical multi-dimensional data, performing data cleaning, standardization, and feature extraction, training a human demand prediction model using a machine learning algorithm, inputting current multi-dimensional data, and outputting human demand prediction results to generate personnel configuration and recruitment plan suggestions.

2. The human resource management system according to claim 1, characterized by, The recruitment module comprises: a data collection unit for collecting current job requirement information and current candidate characteristic data; wherein the current job requirement information includes job responsibilities, skill requirements, experience requirements, and job priorities, and the current candidate characteristic data includes resume data, work experience, and skill assessment results; a matching model construction unit for processing collected historical job requirement information and historical candidate characteristic data using a machine learning algorithm to establish an associated matching model between jobs and candidates; a candidate screening unit for screening target candidates based on current job requirement information and current candidate characteristic data through the associated matching model, generating a matching degree score of target candidates, and outputting a matching ranking result of target candidates.

3. The human resource management system of claim 2, wherein, The recruitment module further comprises: a virtual scene construction unit for customizing a virtual work scene according to the core work scene of the current job requirement information; wherein the virtual work scene contains a task script and an auxiliary tool entry corresponding to the current job requirement information, and the virtual work scene sets a dynamic difficulty level to adjust the scene difficulty according to the initial performance of the target candidate; an AI role interaction unit for configuring an AI-controlled role matching the virtual work scene, the AI-controlled role having preset identity attributes, emotion trigger rules, and branch dialogue libraries, and dynamically outputting text responses, emotion symbols, and reply speeds according to the responses of the target candidate; an interactive process management unit for recording full-process data when the target candidate participates in the virtual work scene interaction; wherein the full-process data includes time dimension data, behavior dimension data, and language dimension data; a behavior data analysis unit for multi-dimensional analysis of the full-process data to convert abstract soft skills into quantifiable behavior indicator scores; wherein the behavior indicators include communication ability, adaptability, initiative, and cultural fit, and the weights of the behavior indicators are dynamically allocated according to the job priority; an evaluation report generation unit for generating a comprehensive evaluation report based on the behavior indicator scores, which includes scene basic information, soft skill evaluation results, cultural fit evaluation results, and recruitment decision suggestions.

4. The human resource management system of claim 3, wherein, The behavior data analysis unit is specifically configured to: preprocess the whole-process data to extract multi-dimensional features related to the evaluation of abstract soft skills; based on a preset behavior index classification system, construct quantitative evaluation models for communication ability, adaptability, initiative, and cultural fit, wherein the communication ability is scored by analyzing the language fluency, response relevance, questioning depth, and listening performance of the target candidate in the interaction; the adaptability is scored by analyzing the coping strategies, adjustment speed, and problem solving effect of the target candidate when facing sudden task script changes, AI character emotional changes, or auxiliary tool failures; the initiative is scored by analyzing the frequency of initiating conversations or tasks, the quantity and quality of improvement suggestions, and the degree of exploring unknown scenarios; the cultural fit is scored by analyzing the matching degree of the target candidate's behavior patterns, language style, and decision preferences with the preset cultural dimensions of the enterprise; according to the job priority in the current job requirement information, dynamically allocate the weights of each behavior index; fuse the weighted scores of each behavior index to generate a comprehensive behavior evaluation score of the target candidate.

5. The human resource management system of claim 1, wherein, The intelligent training module includes: a learning evaluation unit for evaluating the current ability level and learning progress of employees through tests and performance data analysis; a content generation unit for generating training courses suitable for the individual characteristics of employees according to the current ability level, learning progress, and preset training goals; a dynamic adjustment unit for adjusting the difficulty and content of subsequent courses in real time according to the progress and mastery of employees during the learning process.

6. The human resource management system of claim 5, wherein, The intelligent training module further includes: an implicit knowledge evaluation unit for: collecting employee interaction data from enterprise internal collaboration platforms, knowledge management systems, and instant messaging tools; constructing a knowledge flow network based on employee interaction data; wherein the nodes in the knowledge flow network represent employees, the node attributes include employee basic information and explicit skill labels, the network edges represent knowledge exchange relationships, and the edge attributes include direction, weight, and knowledge field labels; the weight is calculated based on exchange depth score, monthly exchange frequency, and knowledge field relevance; calculating the multi-dimensional centrality scores of each employee in each knowledge field using social network analysis algorithms, and calculating the comprehensive centrality scores after standardizing the multi-dimensional centrality scores; wherein the multi-dimensional centrality includes degree centrality, betweenness centrality, and closeness centrality; combining the comprehensive centrality scores and artificial evaluation results to divide the implicit knowledge level of employees in each knowledge field into multiple levels and identify special implicit knowledge roles; fuse the explicit skill data of employees and the implicit knowledge evaluation results to construct a complete knowledge and skill map including field layer, skill layer, and level layer, the map is labeled with knowledge gap information and corresponding job requirement level, and can provide basis for training content generation.

7. The human resource management system of claim 6, wherein The degree centrality of the employee in the ith knowledge field is calculated by calculating a first ratio of the number of knowledge output edges of the employee and the total number of employees in the ith knowledge field minus one, and calculating a product of the first ratio and 100 to obtain the degree centrality of the employee in the ith knowledge field; The betweenness centrality of the employee in the ith knowledge field is calculated by calculating a second ratio of the number of shortest knowledge paths participated by the employee and the number of all shortest paths in the ith knowledge field, and calculating a product of the second ratio and 100 to obtain the betweenness centrality of the employee in the ith knowledge field; The closeness centrality of the employee in the ith knowledge field is calculated by calculating a third ratio of the sum of the shortest path lengths from the employee to all other employees and the total number of employees in the ith knowledge field minus one, and being normalized to a preset score range to obtain the closeness centrality of the employee in the ith knowledge field; The comprehensive centrality score of the employee in the ith knowledge field is calculated by multiplying the degree centrality of the employee in the ith knowledge field, the betweenness centrality of the employee in the ith knowledge field, and the closeness centrality of the employee in the ith knowledge field by corresponding weight coefficients and summing them up.

8. The human resource management system of claim 7, wherein, The tacit knowledge evaluation unit is further configured to: According to the input employee identifier, display the explicit skill gap and the implicit skill gap of the employee corresponding to the employee identifier, and recommend training content based on the explicit skill gap and the implicit skill gap; wherein the training content includes matching the high-level tacit knowledge employee in the ith knowledge field as a mentor for case teaching, or organizing external experts to share training activities combined with internal experience extraction.

9. The human resource management system of claim 1, wherein, The performance evaluation module includes: A data recording unit for writing performance data into a blockchain distributed ledger when a project is completed or a performance target is achieved; A data storage unit for storing performance data in an unalterable manner; A report generation unit for aggregating performance data in a specified period and generating a performance report.

10. The human resource management system of claim 1, wherein, The incentive mechanism module includes: A game element setting unit for defining task completion standards and reward rules, setting up a point acquisition mechanism and a level promotion condition, designing a badge type corresponding to a special achievement, and creating individual challenge tasks and team collaboration tasks; A progress tracking unit for tracking employee task completion, accumulating points and badge numbers, and adjusting employee levels and rankings; A reward management unit for issuing incentive feedback and rewards.

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