Talent evaluation-based person and post matching method and system

Abstract

The invention discloses a person and post matching method and system based on talent evaluation. The method comprises the following steps: step 1, setting a talent evaluation standard; 2, establishing an occupational quality model of the personnel; 3, creating a post model; 4, developing a post matching algorithm; 5, internal employee behavior analysis; step 6, a post recommendation system; step 7, quantitative evaluation and demand analysis; 8, a data processing technology; compared with the prior art, the method has the advantages that the scientificity, objectivity, efficiency and accuracy of personnel and post matching are improved, personalized recommendation of employees is realized, the efficiency of talent recognition is improved, and the method has remarkable superiority.

Description

Technical Field

[0001] This technical solution mainly involves the "field of human resource management technology" and the "field of computer science technology", and specifically is a person-job matching method and system based on talent assessment. Background Art

[0002] Human resource management is a crucial component of modern enterprise management. Person-job matching is a key issue within this approach. Person-job matching involves selecting the most suitable person for a position based on job requirements and individual qualifications to ensure fulfillment of job responsibilities and the full utilization of the individual's value. Traditional methods of matching people based primarily on academic qualifications and experience no longer meet the needs of modern enterprises. Therefore, methods based on talent assessment are becoming a new trend. Furthermore, advances in computer science and psychology are providing new technical tools for matching people. Computer technology enables large-scale data processing and the implementation of person-job matching algorithms, while psychological technology provides scientific assessment methods and models to evaluate personnel qualities and capabilities.

[0003] Existing approaches to matching people with jobs based on talent assessment primarily involve the following: First, by setting talent assessment criteria and assessing multi-dimensional employee information, such as intelligence, personality, and values, a professional competency model is constructed. Next, based on the characteristics of each position, a job model is created, including job responsibilities and job requirements. Finally, the assessed talent is matched with the position to produce a job matching report. Additionally, some methods analyze employees' daily behavior to generate a skill profile and recommend suitable positions based on job requirements. This approach can help companies better understand employees' abilities and characteristics, thereby achieving more accurate job matching.

[0004] While existing approaches to matching people with jobs based on talent assessments have addressed this issue to a certain extent, several issues remain. First, existing methods rely primarily on subjective evaluations and empirical judgments, lacking scientificity and objectivity. Second, existing methods are inefficient when processing large amounts of data and fail to meet the needs of businesses. Finally, existing methods often lack depth and accuracy when assessing personnel qualities and abilities, failing to fully reflect their true capabilities. Summary of the Invention

[0005] The purpose of the present invention is to provide a person-job matching method and system based on talent assessment, which solves the following technical problems: 1. Improve the scientific nature and objectivity of job matching; 2. Improve the efficiency of matching people with jobs; 3. Comprehensively assess the true capabilities of personnel; 4. Solve the problem of matching internal employees with positions; 5. Improve the scientific nature and effectiveness of talent identification.

[0006] To achieve the above object, the present invention provides the following technical solutions: A person-job matching method based on talent assessment includes the following steps: Step 1: Establish talent assessment standards. Based on the needs of the company and the requirements of the position, establish talent assessment standards covering multiple dimensions, including intelligence, personality, values, work skills, and experience, in order to comprehensively and accurately assess the quality, characteristics, and abilities of personnel. Intelligence assessment is conducted through logical reasoning and mathematical calculation tasks; personality assessment is conducted through questionnaires and behavioral observation; values assessment is conducted through value questionnaires and scenario simulations; work skills assessment is conducted through practical operations and simulation training; and experience assessment is conducted through resume screening and interview questions and answers. Step 2: Establish a professional quality model for personnel. Utilizing computer and psychological techniques, and based on the talent assessment standards, conduct a scientific and objective assessment of personnel's characteristics and abilities. This model reflects personnel performance across various dimensions. This model quantifies various quality indicators, providing data support for subsequent person-job matching and avoiding biases based on subjective evaluation and empirical judgment. Step 3: Create a job model. Based on the characteristics of each job, create a corresponding job model. The job model includes job responsibilities, requirements, work content, work environment information, and specific requirements for personnel quality and ability, so as to accurately reflect the real needs of the job and provide accurate job information support for matching people with jobs. Step 4: Develop a job matching algorithm. Using an efficient matching algorithm, combined with the employee's professional quality model and job model, calculate the match between the employee and the job. The matching algorithm considers the weight and importance of each evaluation dimension and generates a detailed job matching report. The report includes a match score, strengths analysis, and weaknesses analysis, improving the scientificity and objectivity of job matching. Step 5: Internal employee behavior analysis: By collecting and analyzing daily employee behavior data, using data mining and machine learning techniques, we generate specific work skill profiles. This behavior data includes work logs, performance data, and collaboration records. The work skill profiles comprehensively reflect employees' true capabilities and work performance, providing a reliable basis for job recommendations. Step 6: The job recommendation system uses a recommendation algorithm based on the employee's work skill profile and job model, taking into account the employee's strengths, interests, career development direction, and actual job requirements. It recommends suitable jobs to employees, accurately matching their personal abilities with the job, and promoting their performance and growth in their new positions. Step 7: Quantitative evaluation and demand analysis: By building an indicator system and quantitative models, we conduct quantitative evaluation and analysis of personnel's professional qualities and the actual needs of the position. Utilizing statistical analysis and data visualization technology, we help companies better identify and select job candidates, improve the scientific nature and effectiveness of talent identification, and provide data support for human resource decision-making. Step 8: Data processing technology: This technology uses large-scale data processing technology, distributed computing, and cloud computing architecture to efficiently process and analyze the data of large amounts of personnel, improve the efficiency of job matching, meet the needs of enterprises in large-scale talent assessment and matching, support multi-concurrency and multi-tasking processing, and ensure system stability and responsiveness. Step 9: Feedback and Optimization. Based on the results of job matching and recommendation, collect actual performance and feedback data of people and jobs, and continuously optimize and improve talent assessment standards, professional quality models, job models, and matching algorithms. This will improve the accuracy and reliability of the system, form a positive feedback mechanism, and ensure the continued effectiveness of person-job matching. Step 10: Security and Privacy: Establish a comprehensive data security and privacy protection mechanism, encrypt the storage and transmission of personnel assessment data, behavioral data, and matching results, formulate strict permission management and access control, comply with relevant laws and regulations, ensure the security of personal information, and enhance the credibility of the system and user confidence; Preferably, the setting of the talent assessment standards includes customized adjustment of the weights and contents of each assessment dimension according to the specific needs of different positions, and through expert interviews, job analysis, and corporate culture research methods, ensuring that the assessment standards are highly consistent with the actual needs of the enterprise, improving the applicability and accuracy of the assessment results, and solving the problem of the assessment standards in the existing technology being general and inaccurate.

[0007] Preferably, the step of establishing a personnel professional quality model includes using artificial intelligence and deep learning technology to extract features and recognize patterns in personnel assessment data, and establish a model capable of self-learning and iterative optimization, so that the model can continuously improve the assessment accuracy and predictive ability of personnel quality characteristics and capabilities as the data increases and time passes, overcoming the deficiency of existing static models that are difficult to adapt to changes.

[0008] Preferably, the step of developing a job matching algorithm includes introducing a multi-objective optimization algorithm, taking into account multiple goals such as personnel and job matching, team structure optimization, and long-term enterprise development, and using genetic algorithms and particle swarm algorithm intelligent optimization technologies to find the global optimal matching solution, avoid local optimality and matching instability problems, and improve the overall efficiency of the matching results.

[0009] Preferably, the step of analyzing internal employee behavior further includes sentiment analysis and social network analysis. By analyzing employees' emotional expressions, communication methods, and collaborative relationships at work, we can gain an in-depth understanding of employees' teamwork ability, leadership potential, and organizational adaptability, enrich the content of work skill portraits, and provide a more comprehensive basis for job recommendations and talent development.

[0010] Preferably, in the steps of quantitative assessment and demand analysis, a standardized indicator system for job capability requirements is established, and job requirements are dynamically updated and calibrated through industry benchmarks and big data analysis to ensure that the job model reflects industry development trends and market demand changes, providing forward-looking support for the company's talent strategy.

[0011] Preferably, the application of the data processing technology includes the use of real-time data stream processing technology to perform real-time analysis and processing of personnel assessment data and behavioral data, support real-time job matching and recommendation, provide technical support for the company's flexible employment and rapid response, and solve the problem of insufficient timeliness in batch processing mode.

[0012] Preferably, in the security and privacy assurance step, blockchain technology is used to record and trace data access and operations to ensure that the data cannot be tampered with and the operations are traceable. At the same time, differential privacy and federated learning technologies are used to protect personal privacy data from being leaked, thereby achieving a balance between data security and data utilization.

[0013] Preferably, in the feedback and optimization step, a feedback mechanism involving user participation is established, allowing employees and managers to evaluate and make suggestions on matching results and recommended positions. Using this feedback information, machine learning algorithms are used to adjust and improve models and algorithms, thereby enhancing the user-friendliness and applicability of the system and meeting personalized and diversified needs.

[0014] A person-job matching system based on talent assessment, comprising: Data input module, used to collect and input talent assessment standards, personnel assessment data, internal employee behavior data and job information; The model building module uses computer technology, artificial intelligence and psychology technology to establish personnel professional quality models and job models to reflect the multi-dimensional characteristics of personnel and positions; The matching algorithm module uses efficient matching algorithms and intelligent optimization technology to calculate the matching degree between personnel and positions and generate a position matching report; The behavior analysis module generates employee work skill profiles by analyzing internal employees' daily behavior data, reflecting their true abilities and work performance. The job recommendation module recommends suitable jobs to employees based on their work skill profiles and job models, achieving an accurate match between employees' personal abilities and jobs; The data processing module uses large-scale data processing and real-time data stream processing technologies to efficiently process and analyze large amounts of data, thereby improving the efficiency and accuracy of job matching; Feedback and optimization module, which collects and processes feedback information on matching results and continuously optimizes and improves models and algorithms; The security and privacy protection module is responsible for the secure storage, transmission and access control of data, ensuring the security and privacy of personal and corporate data. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A flow chart of a person-job matching method based on talent assessment provided by the present invention; Figure 2 This is a system diagram of a person-job matching system based on talent assessment provided by the present invention.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. Scientificity and objectivity: This invention sets talent assessment standards and constructs a professional quality model, using computer technology and psychological technology to achieve scientific assessment of multi-dimensional information on people, avoiding the deviation of subjective evaluation and experience judgment, and improving the scientificity and objectivity of person-job matching.

[0017] 2. High efficiency and large-scale processing capability: The present invention adopts computer technology to realize large-scale data processing and the implementation of person-job matching algorithms, which greatly improves the efficiency of person-job matching and can meet the needs of enterprises.

[0018] 3. Depth and accuracy: This invention generates work skill profiles by analyzing employees' daily behaviors and recommends suitable positions based on job requirements. It can more comprehensively reflect the true capabilities of personnel and improve the depth and accuracy of person-job matching.

[0019] 4. Personalized recommendations: This invention analyzes the skill profiles of internal employees and recommends suitable positions and work intensity for them, which is conducive to giving full play to the strengths of each employee, maximizing the personal abilities of employees, and realizing personalized recommendations for employees.

[0020] 5. Improve the efficiency of talent identification: This invention can help enterprises quantify and evaluate the professional qualities of personnel and the actual needs of positions, improve the efficiency and accuracy of person-job matching, thereby improving the scientificity and effectiveness of talent identification and providing a reliable basis for enterprises to efficiently select job candidates. DETAILED DESCRIPTION

[0021] The following is a combination of the embodiments of the present invention Figure 1 and attached Figure 2 , clearly and completely describing the technical solutions in the embodiments of the present invention. Obviously, the implementation regulations described are only part of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0022] Step 1: Establish talent assessment standards. Establish scientific and objective talent assessment standards based on the needs of the company and the requirements of the position. These standards can include multiple dimensions such as intelligence, personality, values, work skills, and experience, so as to comprehensively and accurately assess the qualities, characteristics, and abilities of individuals. For example, intelligence can be assessed through tasks such as logical reasoning and mathematical calculations; personality can be assessed through questionnaires and behavioral observations; values can be assessed through values questionnaires and scenario simulations; work skills can be assessed through practical operations and simulation training; and experience can be assessed through resume screening and interview questions.

[0023] Step 2: Establish a professional competency model for personnel. Leveraging computer and psychological techniques, and based on talent assessment standards, establish a professional competency model for personnel. This model can reflect personnel characteristics and capabilities across various dimensions, providing a basis for subsequent job matching. For example, data mining and machine learning methods can be used to establish a professional competency model based on personnel assessment results.

[0024] Step 3: Create a job model. Create a corresponding job model based on the characteristics of each job. This model can include information such as job responsibilities, requirements, work content, and work environment to accurately reflect the actual needs of the job. For example, job analysis and task decomposition can be used to create a job model.

[0025] Step 4: Develop a job matching algorithm. Develop an efficient algorithm to match qualified candidates with positions. This algorithm can calculate the degree of fit between the candidate and the position based on the candidate's professional competency model and the characteristics of the position, thereby generating a job matching report. For example, a job matching algorithm can be developed using graph matching algorithms or optimization algorithms.

[0026] Step 5: Internal Employee Behavior Analysis. By analyzing the daily behavior of internal employees, a detailed work skills profile is generated. This profile reflects the employee's true capabilities and work performance, providing a basis for subsequent job recommendations. For example, work log analysis and behavioral observation can be used to generate a work skills profile.

[0027] Step 6: Job recommendation system. Recommend suitable positions to employees based on their skill profile and job requirements. This system can take into account employees' strengths and interests, as well as the actual job requirements, to accurately match employees' individual abilities with positions. For example, job recommendation algorithms and user interface design can be used to develop a job recommendation system.

[0028] Step 7: Quantitative Assessment and Needs Analysis. This quantitative analysis can help companies better identify and select candidates, improving the scientific nature and effectiveness of talent identification. For example, quantitative assessment and needs analysis can be conducted through data statistics and the development of an indicator system.

[0029] Step 8: Data Processing Technology. Use large-scale data processing technology to evaluate and match large numbers of candidates. This technology can significantly improve the efficiency of job matching and meet enterprise needs. For example, large-scale data processing can be achieved through distributed computing and cloud computing.

[0030] The above are the specific steps of this embodiment. Through these steps, it is possible to achieve person-job matching based on talent assessment, improve the scientificity and objectivity of person-job matching, improve the efficiency of person-job matching, comprehensively evaluate the true capabilities of personnel, solve the problem of matching internal employees with positions, improve the scientificity and effectiveness of talent identification, and provide a reliable basis for enterprises to efficiently select job candidates. Specific embodiments The specific implementation steps of the method and system of the present invention will be described in detail below with reference to specific embodiments.

[0032] Step 1: Set talent assessment criteria 1. Enterprise demand research: Strategic Goal Analysis: Communicate with senior management to understand the company's long-term strategic goals, core values and cultural concepts, and clarify the overall direction of talent assessment standards.

[0033] Job requirements collection: Collect job descriptions and recruitment needs from various departments, and learn in detail about the responsibilities, requirements, working environment, and team composition of each position.

[0034] 2. Determine the evaluation dimensions: Based on the results of demand analysis, talent assessment standards are set, covering multiple dimensions such as intelligence, personality, values, work skills, and experience.

[0035] Intelligence dimension: including logical reasoning ability, mathematical calculation ability, language comprehension ability, spatial imagination ability, etc.

[0036] Personality dimensions: including extraversion, conscientiousness, emotional stability, openness, agreeableness, etc.

[0037] Value dimension: including work motivation, teamwork awareness, innovative spirit, professional ethics, etc.

[0038] Work skills dimension: Set specific skill requirements based on job requirements, such as programming ability, project management ability, market analysis ability, etc.

[0039] Experience dimension: including educational background, years of work experience, industry experience, past performance, etc.

[0040] 3. Develop assessment tools: Select appropriate assessment tools and methods for each dimension.

[0041] Intelligence assessment: Standardized intelligence test questions are used, such as the Wechsler Adult Intelligence Test (WAIS) and the Raven's Standard Reasoning Test.

[0042] Personality assessment: Use personality questionnaires such as the Big Five Personality Inventory (BFI) and the Minnesota Multiphasic Personality Inventory (MMPI).

[0043] Value assessment: using value questionnaires, scenario simulation tests, etc.

[0044] Work skills assessment: designing practical operation tasks, case analysis, simulation training, etc.

[0045] Experience assessment: through resume screening, interview questions and answers, background checks, etc.

[0046] 4. Standardization and weight setting: Standardize each evaluation dimension, set scoring standards, and set weights for each dimension based on job requirements to ensure the objectivity and pertinence of the evaluation results.

[0047] Step 2: Establish personnel professional quality model 1. Data collection: Organize candidates to undergo assessments and collect assessment data in various dimensions.

[0048] 2. Data processing: Clean, organize and standardize the evaluation data, and remove outliers and invalid data.

[0049] 3. Feature extraction: Utilize statistical analysis and data mining techniques to extract key features from the evaluation data.

[0050] 4. Model building: Use machine learning algorithms (such as decision trees, support vector machines, neural networks, etc.) to build a professional quality model for personnel.

[0051] Model input: feature vector of evaluation data.

[0052] Model output: personnel scores in various dimensions and overall quality scores.

[0053] 5. Model validation and optimization: Use methods such as cross-validation and K-fold validation to evaluate the accuracy and stability of the model, adjust model parameters, and improve model performance.

[0054] Step 3: Create a job model 1. Job information collection: Collect job descriptions, work content, qualifications, performance indicators, and other information for each job.

[0055] 2. Job Analysis: Job decomposition: Break down the job content of a position into specific tasks and responsibilities.

[0056] Competency requirements analysis: Analyze the required knowledge, skills, abilities and other attributes (KSAOs) based on the job tasks.

[0057] 3. Establish a job model: Characterization: Quantify job requirements into feature vectors corresponding to the personnel professional quality model.

[0058] Weight setting: Set the weight of each capability requirement based on the importance of the position and corporate strategy.

[0059] 4. Job database construction: Establish a database of job models to facilitate subsequent matching and query.

[0060] Step 4: Develop a job matching algorithm 1. Algorithm selection: Choose an appropriate matching algorithm, such as graph matching algorithm, collaborative filtering, cosine similarity, etc.

[0061] 2. Matching calculation: Similarity calculation: Calculate the similarity between the characteristic vectors of the personnel's professional quality model and job model.

[0062] Comprehensive score: Combine the weights of each dimension to calculate the comprehensive matching score.

[0063] 3. Algorithm optimization: Multi-objective optimization: Considering factors such as matching degree, personnel interests, team composition, etc., genetic algorithm or particle swarm optimization algorithm is used to find the optimal matching solution.

[0064] Real-time response: Optimize the algorithm's computing performance to ensure that matching results can be given in real time in the case of large-scale data.

[0065] 4. Matching report generation: Generate a detailed job matching report, including matching score, strengths and weaknesses analysis, development suggestions, etc.

[0066] Step 5: Internal employee behavior analysis 1. Data collection: Collect employees’ daily behavior data from internal systems, such as: Work log: project progress, task completion status, etc.

[0067] Performance data: performance appraisal results, KPI completion status, etc.

[0068] Collaboration records: team collaboration status, communication records, etc.

[0069] Learning records: training attendance, learning performance, etc.

[0070] 2. Data preprocessing: Clean and anonymize data to protect employee privacy.

[0071] 3. Behavioral feature extraction: Quantitative analysis: statistical indicators such as workload, number of results, and error rate.

[0072] Qualitative analysis: Use natural language processing technology to conduct sentiment analysis, topic analysis, etc. on text data.

[0073] 4. Work Skills Profile Generation: Model building: Use machine learning algorithms to map behavioral characteristics into skill indicators.

[0074] Portrait description: It forms the employee's performance in various skills, highlighting the strengths and potential.

[0075] Step 6: Job Recommendation System 1. Recommendation algorithm design: Collaborative filtering: Recommend potentially suitable positions based on the career development paths of similar employees.

[0076] Content recommendation: Recommend positions based on the matching degree between the employee's work skill profile and the job model.

[0077] 2. Multiple factors to consider: Personal wishes: Consider employees' career interests and development expectations.

[0078] Enterprise needs: Consider the enterprise's strategic layout and talent reserve needs.

[0079] 3. Recommendation results display: User interface design: Provide a friendly interactive interface to display recommended positions and their matching reasons.

[0080] Interactive feedback: Allow employees to provide feedback on recommendation results and adjust the recommendation algorithm.

[0081] 4. Follow-up and support: Provide training and support to employees who accept job recommendations to promote their success in the new position.

[0082] Step 7: Quantitative evaluation and demand analysis 1. Construction of indicator system: Personnel evaluation indicators: Establish quantitative indicators for each quality dimension.

[0083] Job requirement indicators: Establish quantitative indicators of job competency requirements.

[0084] 2. Data Statistical Analysis: Personnel quality analysis: Use statistical methods to analyze the distribution, mean, and differences of personnel assessment results.

[0085] Job demand analysis: Analyze the standards and changing trends of job demand in the industry.

[0086] 3. Data visualization: Use charts, dashboards, and other forms to intuitively display analysis results and support human resource management decisions.

[0087] 4. Demand matching: Through quantitative indicators, the gap between personnel quality and job requirements is compared to provide a basis for training and development.

[0088] Step 8: Large-scale data processing technology 1. Technical architecture design: Distributed computing: Using frameworks such as Hadoop and Spark to support the storage and computing of large-scale data.

[0089] Cloud computing platform: uses cloud services to provide flexible computing resources.

[0090] 2. Data processing flow: Real-time data processing: Real-time collection and analysis of assessment data and behavioral data to support immediate decision-making.

[0091] Batch data processing: Regularly analyze historical data to identify long-term trends.

[0092] 3. Performance optimization: Storage optimization: Choose appropriate storage solutions based on data characteristics, such as relational databases, NoSQL databases, and cloud storage.

[0093] Computing optimization: Use memory computing and parallel computing technologies to speed up data processing.

[0094] 4. System monitoring and maintenance: Monitoring system: real-time monitoring of system performance and resource usage, and early warning of abnormal conditions.

[0095] Fault-tolerant mechanism: Design data backup and failover solutions to ensure system stability and reliability.

[0096] Step 9: Feedback and Optimization 1. Performance feedback collection: Track the performance of matching personnel in their positions and collect feedback from managers and employees.

[0097] 2. Data Analysis: Effectiveness evaluation: Evaluate the effectiveness of matching results and analyze success and failure cases.

[0098] 3. Model optimization: Adjust evaluation criteria: Based on feedback, revise evaluation criteria and weights.

[0099] Improved algorithm: Optimize the matching algorithm to improve matching accuracy.

[0100] 4. Continuous improvement: Establish a closed-loop feedback mechanism to continuously improve system performance and user satisfaction.

[0101] Step 10: Security and Privacy 1. Data encryption: Encrypt sensitive data during storage and transmission to prevent unauthorized access.

[0102] 2. Permission management: Set strict permission controls to ensure that only authorized personnel can access corresponding data and functions.

[0103] 3. Logging and auditing: Record system operation logs and conduct regular audits to prevent internal abuse and external attacks.

[0104] 4. User notification and consent: When collecting personal data, clearly inform the user of the purpose and obtain their consent.

[0105] System integration and deployment.

[0106] 1. System Integration: Module integration: Integrate data input module, model building module, matching algorithm module, behavior analysis module, job recommendation module, etc. into a unified platform.

[0107] Interface development: Develop interfaces with enterprise human resource management systems (HRMS), recruitment systems, performance management systems, etc. to achieve seamless data integration.

[0108] 2. System Implementation Hardware environment Server cluster: Deployed on a high-reliability server cluster, supporting distributed computing and storage.

[0109] Data storage devices: Use high-performance databases and storage devices to meet the storage needs of large-scale data.

[0110] Software Environment Operating system: Linux or other stable server operating systems.

[0111] Database: Use a combination of relational databases (such as MySQL) and NoSQL databases (such as MongoDB).

[0112] Data processing framework: Hadoop, Spark and other big data processing frameworks.

[0113] Programming languages: Java, Python, etc., supporting machine learning and data analysis.

[0114] Security software: firewalls, intrusion monitoring systems, security auditing software, etc.

[0115] 3. System deployment: Environmental preparation: Configure the server, network, firewall and other hardware environments according to system requirements.

[0116] Deployment implementation: Install system software, deploy applications, and configure environment variables according to the deployment plan.

[0117] Online operation: After the trial operation is correct, the system will be officially launched and start providing services to the enterprise.

[0118] 4. System testing: Functional testing: Ensure that all functional modules operate normally and meet design requirements.

[0119] Performance testing: Under real-world pressure, the system's response time, throughput, resource usage, and other indicators are tested.

[0120] Security testing: Conduct penetration testing and vulnerability scanning to discover and fix security vulnerabilities.

[0121] Due to the advanced nature of this technical solution, it has broad applications in fields such as human resources management, computer science, and psychology. In the field of human resources management, the present invention can help companies quantify and assess the professional qualities of their employees and the actual requirements of their positions, improving the efficiency and accuracy of person-job matching, thereby enhancing the scientific nature and effectiveness of talent identification and providing a reliable basis for companies to efficiently select job candidates. Especially in large enterprises or group companies with large numbers of employees and a wide variety of positions, the present invention can achieve person-job matching more scientifically and efficiently, improving the company's management efficiency and employee work efficiency. In the field of computer science and technology, the present invention relates to the implementation of large-scale data processing and person-job matching algorithms, which can promote the development of related technologies. In particular, with the continuous development of technologies such as big data and artificial intelligence, how to efficiently process and analyze large-scale data and how to achieve more intelligent person-job matching are currently important research directions in the field of computer science and technology. The application of this invention can provide new ideas and methods for the research and development of related technologies. In the field of psychology and technology, the present invention relates to the construction of talent assessment standards and models, which can promote the development of psychological technology. In particular, with the continuous deepening of psychological research, how to more scientifically and accurately assess the qualities, characteristics, and abilities of personnel is currently an important research direction in the field of psychological technology. The application of this invention can provide new ideas and methods for the application and research of psychology technology. In general, the application prospects of this invention are broad and the market demand is large. It is expected to play an important role in the fields of human resource management, computer science and technology, and psychology technology.

[0122] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to the specific embodiments described. Obviously, many modifications and variations are possible based on the content of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A person-job matching method based on talent assessment, characterized in that: The following steps are involved: Step 1: Establish talent assessment criteria. Based on the company's needs and job requirements, establish talent assessment criteria across multiple dimensions, including intelligence, personality, values, work skills, and experience. Intelligence is assessed through logical reasoning and mathematical calculation tasks; personality is assessed through questionnaires and behavioral observations. Values assessment is conducted through questionnaires and scenario simulations; work skills assessment is conducted through practical operations and simulation training; and experience assessment is conducted through resume screening and interview questions and answers. Step 2: Establish a professional quality model for personnel. Utilize computer technology and psychological techniques to scientifically and objectively evaluate personnel's quality characteristics and abilities based on the talent assessment standards. Establish a professional quality model that reflects personnel's performance in various dimensions and quantify various quality indicators of personnel. Step 3: Create a job model. Based on the characteristics of each job, create a corresponding job model. The job model includes job responsibilities, requirements, work content, work environment information, and specific requirements for personnel quality and ability, so as to accurately reflect the real needs of the job and provide accurate job information support for matching people with jobs. Step 4: Develop a job matching algorithm. Using an efficient matching algorithm, combined with the employee's professional quality model and job model, calculate the match between the employee and the job. The matching algorithm considers the weight and importance of each evaluation dimension and generates a detailed job matching report. The report includes a match score, strengths analysis, and weaknesses analysis, improving the scientificity and objectivity of job matching. Step 5: Internal employee behavior analysis: By collecting and analyzing daily employee behavior data, using data mining and machine learning techniques, we generate specific work skill profiles. This behavior data includes work logs, performance data, and collaboration records. The work skill profiles comprehensively reflect employees' true capabilities and work performance, providing a reliable basis for job recommendations. Step 6: The job recommendation system uses a recommendation algorithm based on the employee's work skill profile and job model, taking into account the employee's strengths, interests, career development direction, and actual job requirements. It recommends suitable jobs to employees, achieving an accurate match between individual abilities and positions. Step 7: Quantitative evaluation and demand analysis: By building an indicator system and quantitative models, we conduct quantitative evaluation and analysis of personnel's professional qualities and the actual needs of the position. Utilizing statistical analysis and data visualization technology, we help companies better identify and select job candidates, improve the scientific nature and effectiveness of talent identification, and provide data support for human resource decision-making. Step 8: Data processing technology: This technology uses large-scale data processing technology, distributed computing, and cloud computing architecture to efficiently process and analyze the data of large amounts of personnel, improve the efficiency of job matching, meet the needs of enterprises in large-scale talent assessment and matching, support multi-concurrency and multi-tasking processing, and ensure system stability and responsiveness. Step 9: Feedback and Optimization. Based on the results of job matching and recommendation, collect actual performance and feedback data of people and jobs, and continuously optimize and improve talent assessment standards, professional quality models, job models, and matching algorithms. This will improve the accuracy and reliability of the system, form a positive feedback mechanism, and ensure the continued effectiveness of person-job matching. Step 10: Security and privacy protection. Establish a complete data security and privacy protection mechanism, encrypt the storage and transmission of personnel assessment data, behavioral data and matching results, formulate strict permission management and access control, comply with relevant laws and regulations, ensure the security of personal information, and enhance the credibility of the system and user confidence.

2. The method for matching people with jobs based on talent assessment according to claim 1, characterized in that: The setting of the talent assessment standards includes customized adjustment of the weights and content of each assessment dimension according to the specific needs of different positions. Through expert interviews, job analysis, and corporate culture research methods, it ensures that the assessment standards are highly consistent with the actual needs of the enterprise, improves the applicability and accuracy of the assessment results, and solves the problem of the assessment standards in existing technologies being general and inaccurate.

3. The method for matching people with jobs based on talent assessment according to claim 1, characterized in that: The steps of establishing a personnel professional quality model include using artificial intelligence and deep learning technology to extract features and recognize patterns in personnel assessment data, and establish a model that can self-learn and iteratively optimize, so that the model can continuously improve the assessment accuracy and predictive ability of personnel quality characteristics and capabilities as the data increases and time passes, overcoming the deficiency of existing static models that are difficult to adapt to changes.

4. The method for matching people with jobs based on talent assessment according to claim 1, characterized in that: The steps of developing the job matching algorithm include introducing a multi-objective optimization algorithm, taking into account multiple goals such as the matching degree between personnel and positions, team structure optimization, and long-term development of the enterprise, and using genetic algorithms and particle swarm algorithm intelligent optimization technologies to find the globally optimal matching solution, avoid local optimality and matching instability problems, and improve the overall benefits of the matching results.

5. The method for matching people with jobs based on talent assessment according to claim 1, characterized in that: The steps of internal employee behavior analysis further include sentiment analysis and social network analysis. By analyzing employees' emotional expressions, communication methods, and collaborative relationships at work, we can gain an in-depth understanding of employees' teamwork ability, leadership potential, and organizational adaptability, enrich the content of their work skill profiles, and provide a more comprehensive basis for job recommendations and talent development.

6. The method for matching people with jobs based on talent assessment according to claim 1, characterized in that: In the steps of quantitative assessment and demand analysis, a standardized indicator system for job capability requirements is established, and job requirements are dynamically updated and calibrated through industry benchmarks and big data analysis to ensure that the job model reflects industry development trends and changes in market demand.

7. The method for matching people with jobs based on talent assessment according to claim 1, characterized in that: The application of the data processing technology includes the use of real-time data stream processing technology to perform real-time analysis and processing of personnel assessment data and behavioral data, support real-time job matching and recommendation, and provide technical support for the company's flexible employment and rapid response.

8. The method for matching people with jobs based on talent assessment according to claim 1, characterized in that: In the security and privacy protection steps, blockchain technology is used to record and trace data access and operations to ensure that the data cannot be tampered with and the operations are traceable. At the same time, differential privacy and federated learning technologies are used to protect personal privacy data from being leaked, thereby achieving a balance between data security and data utilization.

9. The method for matching people with jobs based on talent assessment according to claim 1, characterized in that: In the feedback and optimization step, a user-participated feedback mechanism is established to allow employees and managers to evaluate and make suggestions on matching results and recommended positions. Using this feedback information, machine learning algorithms are used to adjust and improve models and algorithms, thereby enhancing the user-friendliness and applicability of the system and meeting personalized and diversified needs.

10. A person-job matching system based on talent assessment, characterized in that: include: Data input module, used to collect and input talent assessment standards, personnel assessment data, internal employee behavior data and job information; The model building module uses computer technology, artificial intelligence and psychology technology to establish personnel professional quality models and job models to reflect the multi-dimensional characteristics of personnel and positions; The matching algorithm module uses efficient matching algorithms and intelligent optimization technology to calculate the matching degree between personnel and positions and generate a position matching report; The behavior analysis module generates employee work skill profiles by analyzing internal employees' daily behavior data, reflecting their true abilities and work performance. The job recommendation module recommends suitable jobs to employees based on their work skill profiles and job models, achieving an accurate match between employees' personal abilities and jobs; The data processing module uses large-scale data processing and real-time data stream processing technologies to efficiently process and analyze large amounts of data, thereby improving the efficiency and accuracy of job matching; Feedback and optimization module, which collects and processes feedback information on matching results and continuously optimizes and improves models and algorithms; The security and privacy protection module is responsible for the secure storage, transmission and access control of data, ensuring the security and privacy of personal and corporate data.

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