Human resource post digital evaluation system based on big data and AI

Abstract

The invention relates to a human resource post digital evaluation system based on big data and AI. The human resource post digital evaluation system comprises a data acquisition and processing module, a post feature database module, an AI model construction and adaptive learning module, a multivariate comprehensive evaluation module and a user interaction and report generation module. And an NLP text analysis unit, a feature engineering and feature storage unit, a multi-task learning model training unit, an incremental learning and model updating unit and a feedback integration and optimization unit are arranged in the AI model construction and adaptive learning module. The method has the advantage that the limitation that a traditional evaluation mode depends on subjective experience is avoided. The self-adaptive learning engine can capture the change of post requirements in real time, so that the evaluation standard can be dynamically adjusted, the evaluation result is ensured to always meet the actual employment requirements of an enterprise, the automation level of the evaluation process is improved, and HR personnel can be liberated from tedious data processing and more concentrate on the effect of talent management work at the strategic level.

Description

Technical Field

[0001] This application relates to the technical field of human resources, and in particular to a digital evaluation system for human resources positions based on big data and AI. Background Technology

[0002] Human resource job evaluation is a core function of corporate human resource management. It aims to comprehensively analyze and assess the responsibilities, job content, skill requirements, workload, work environment, and contribution to corporate strategic goals of each position through systematic and scientific methods, in order to determine the relative value and importance of each position within the organization. This evaluation typically employs methods such as job analysis questionnaires, job observation, interviews, and rating scales (e.g., Hay Group assessment, job evaluation systems). The results can be used for salary system design, promotion path planning, performance appraisal standard development, and human resource allocation optimization. Human resource job evaluation emphasizes fairness, objectivity, and operability, effectively promoting internal equity, motivating employee enthusiasm, and improving organizational efficiency and management level. It is a crucial foundation for modern enterprises to build a scientific human resource management system.

[0003] However, existing technologies for evaluating human resource positions suffer from several problems, including reliance on human experience leading to high subjectivity, fixed evaluation standards that are difficult to adapt to business changes, low data processing efficiency and weak ability to integrate multi-source information, lagging model updates that fail to incorporate emerging skill requirements in a timely manner, and a lack of automated tools that result in a heavy workload for HR. In addition, the evaluation results are presented in a single way, making it difficult to intuitively assist in decision-making. Summary of the Invention

[0004] To address the problems of existing technologies in human resource job evaluation, which rely heavily on human experience, leading to high subjectivity, low data processing efficiency, and weak ability to integrate multi-source information, this application provides a digital evaluation system for human resource positions based on big data and AI.

[0005] This application provides a digital evaluation system for human resources positions based on big data and AI, which adopts the following technical solution: A digital evaluation system for human resources positions based on big data and AI, including a data acquisition and processing module, a job feature database module, an AI model construction and adaptive learning module, a multi-dimensional comprehensive evaluation module, and a user interaction and report generation module;

[0006] The AI ​​model building and adaptive learning module is internally configured with an NLP text parsing unit, a feature engineering and feature storage unit, a multi-task learning model training unit, an incremental learning and model update unit, and a feedback integration and optimization unit.

[0007] The output of the data acquisition and processing module is connected to the input of the job feature database module and the raw data input of the AI ​​model construction and adaptive learning module, respectively.

[0008] The feature data output end of the job feature database module is connected to the feature input interface of the AI ​​model construction and adaptive learning module and the benchmark data input end of the multivariate comprehensive evaluation module;

[0009] The model parameter output terminal of the AI ​​model construction and adaptive learning module is connected to the model configuration interface of the multivariate comprehensive evaluation module;

[0010] The evaluation result output terminal of the multi-dimensional comprehensive evaluation module is connected to the data receiving terminal of the user interaction and report generation module;

[0011] The feedback data output of the user interaction and report generation module is connected back to the iterative optimization interface of the AI ​​model building and adaptive learning module.

[0012] In a preferred embodiment, the data acquisition and processing module is internally equipped with a multi-source data access interface, a data cleaning and standardization module, a structured conversion unit, a data quality monitoring component, a data desensitization and access control module, and a real-time data processing engine.

[0013] In a preferred embodiment, the job feature database module internally includes a job basic information database, a skill tag system, a job template library, a dynamic feature update unit, a job association analysis tool, and a skill map construction unit.

[0014] In a preferred embodiment, the NLP text parsing unit performs deep semantic extraction on unstructured text in human resources scenarios. The core process involves a three-tiered workflow of "text preprocessing - semantic encoding - feature enhancement" to output structured features. First, the text preprocessing stage uses regular expressions to filter special symbols and stop words, and WordPiece segmentation divides the Chinese text into sub-word units. For multilingual scenarios in multinational corporations, a multilingual BERT model is integrated to achieve unified encoding for 10 languages, including Chinese, English, Japanese, and Korean. Second, the semantic encoding layer, based on a pre-trained BERT-base model, performs context-aware encoding on the input text: key phrases in job descriptions such as "proficient in Python" and "over 5 years of project management experience" are transformed into 768-dimensional vectors, and sentence vector pooling is used to generate the overall semantic representation of the text. To enhance professional domain adaptability, a domain data fine-tuning strategy is adopted: historical job data from the enterprise is used for secondary training of BERT to optimize the semantic capture capabilities of "skill terminology" and "industry-specific expressions." Finally, the feature enhancement module achieves structured output through keyword extraction and semantic similarity calculation: it extracts the top 20 core keywords from the encoding results based on the TextRank algorithm, and links entities in conjunction with the industry skill tag library to generate standardized skill tags; at the same time, it calculates the matching degree between keyword vectors and job template vectors through cosine similarity to quantify the weight of each skill in the text.

[0015] The dynamic weight fusion formula for the NLP text parsing unit is as follows:

[0016] ;

[0017] in:

[0018] w i This represents the final weight of the i-th keyword (value range 0~1).

[0019] α represents the balance coefficient (value 0.3, determined through grid search optimization, used to adjust the contribution ratio of statistical features and semantic features).

[0020] TF-IDF i This represents the term frequency-inverse document frequency value of the i-th keyword (reflecting the statistical importance of the word in the text);

[0021] Attn i This represents the attention weight corresponding to the i-th keyword output by the BERT model (reflecting the importance of the word in the context semantics, with a value ranging from 0 to 1).

[0022] Sim i This represents the semantic similarity between the i-th keyword and the core skills in the job template library.

[0023] In a preferred embodiment, the feature engineering and feature storage unit is internally provided with a structured data processing module, a text feature conversion module, a feature fusion module, and a feature storage module.

[0024] In a preferred embodiment, the multi-task learning model training unit is internally configured with a shared encoder-task decoder architecture, a two-stage optimization strategy, a dynamic task weight mechanism, and an optimizer and learning rate scheduling module.

[0025] The dynamic weight adjustment formula for the training unit of the multi-task learning model based on loss volatility and task relevance is as follows:

[0026] ;

[0027] in:

[0028] The dynamic weight of the k-th task (job value assessment / skill matching degree / performance potential) is represented (value range 0~1).

[0029] This represents the reciprocal of the precision parameter for the k-th task (calculated using the Gaussian likelihood estimation of the task loss, reflecting the task difficulty; a larger value indicates a more difficult task, requiring a higher weight).

[0030] γ represents the volatility sensitivity coefficient (fixed at 0.8, controlling the intensity of the impact of loss volatility on the weights).

[0031] This represents the loss variance of the k-th task in the last 100 training rounds (reflecting loss stability; the larger the variance, the more the weights are reduced, thus preventing the model from being dominated by unstable tasks).

[0032] ρ k This represents the average correlation coefficient between the k-th task and other tasks (calculated using the Pearson correlation coefficient, with a value ranging from -1 to 1; positively correlated tasks are given higher weights to enhance the synergistic effect).

[0033] In a preferred embodiment, the incremental learning and model update unit is internally configured with a data filtering and drift detection module, an incremental training and parameter protection module, a model compression module, and a version management module.

[0034] The formula for the adaptive parameter protection strength of the incremental learning and model update unit based on parameter importance and data drift is as follows:

[0035] ;

[0036] in:

[0037] λ pThe protection strength for the p-th model parameter (value range 0~5, the larger the value, the more difficult it is for the parameter to be modified in incremental training);

[0038] α is the balance coefficient (fixed at 0.7, representing the proportion of the control parameter's contribution to stability).

[0039] I p The historical task importance of the p-th parameter (calculated using the EWC algorithm, reflecting the parameter's contribution to the accuracy of the historical task, with a value range of 0 to 1);

[0040] γ is the drift sensitivity coefficient (fixed at 0.5, adjusting the magnitude of the impact of data drift on the protection strength);

[0041] D p The drift of the p-th parameter associated feature (calculated by JS divergence to measure the difference in distribution between new and old data, with a value range of 0 to 1);

[0042] Var(θ p ) represents the variance of the p-th parameter in historical training (reflecting parameter stability; the smaller the variance, the more robust the parameter is, requiring higher protection strength; the value range is 0~1).

[0043] In a preferred embodiment, the feedback integration and optimization unit is internally configured with a feedback acquisition module, a case analysis layer, a parameter tuning module, and a rule base update module.

[0044] In a preferred embodiment, the multi-dimensional comprehensive evaluation module is internally configured with an evaluation index system, a weight allocation mechanism, an evaluation model engine, an evaluation result calibration component, an evaluation cycle management tool, and a cross-dimensional comparative analysis module.

[0045] In a preferred embodiment, the user interaction and report generation module is internally configured with a visual operation interface, a report template library, a data export component, a feedback collection unit, a user permission management module, and a report automation scheduling component.

[0046] In summary, this application includes at least one of the following beneficial technical effects:

[0047] 1. In this invention, through the model training unit of the AI ​​model construction and adaptive learning module, the system can integrate multi-source data for in-depth analysis, avoiding the limitations of traditional evaluation methods that rely on subjective experience. The adaptive learning engine captures changes in job requirements in real time, allowing evaluation criteria to be dynamically adjusted to ensure that the evaluation results always align with the company's actual staffing needs. The algorithm optimization component continuously iterates on the underlying model, reducing the workload of manually adjusting parameters and improving the automation level of the evaluation process. This frees HR personnel from tedious data processing, allowing them to focus more on strategic talent management.

[0048] 2. In this invention, the model evaluation tool periodically checks model performance. Once a deviation is detected, it automatically triggers an optimization process to prevent distorted evaluation results due to model aging. The dynamic feature update unit continuously crawls job requirement data from external recruitment platforms, promptly incorporating emerging skill requirements into the evaluation system to help companies plan their talent development in advance. The feedback collection unit combines user feedback to continuously calibrate model parameters, making the system functions more aligned with HR operating habits and improving the tool's practicality and ease of use.

[0049] 3. In this invention, the system enhances the overall efficiency of human resource management through the collaborative work of its various modules. The data acquisition and processing module's ability to integrate multi-source data makes the evaluation criteria more comprehensive and richer; the skill map constructed from the job characteristic database provides a visual analysis tool for talent inventory; the cross-dimensional comparison function of the multi-dimensional comprehensive evaluation module helps managers quickly identify employees' strengths and weaknesses. The user interaction and report generation module simplifies the output process of evaluation results, and various automated reports allow decision-makers to intuitively grasp the talent situation, providing reliable support for human resource decisions such as recruitment, training, and promotion, and promoting the overall improvement of the enterprise's talent management level. Attached Figure Description

[0050] Figure 1 This is an overall system block diagram of the present invention;

[0051] Figure 2 This is a system block diagram of the AI ​​model construction and adaptive learning module of the present invention.

[0052] Figure labeling: 1. Data acquisition and processing module; 2. Job feature database module; 3. AI model construction and adaptive learning module; 4. Multivariate comprehensive evaluation module; 5. User interaction and report generation module; 6. NLP text parsing unit; 7. Feature engineering and feature storage unit; 8. Multi-task learning model training unit; 9. Incremental learning and model update unit; 10. Feedback integration and optimization unit. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0054] The following is in conjunction with the appendix Figures 1-2 This application will be described in further detail.

[0055] See Figure 1-2A digital evaluation system for human resources positions based on big data and AI includes a data acquisition and processing module 1, a job feature database module 2, an AI model construction and adaptive learning module 3, a multi-dimensional comprehensive evaluation module 4, and a user interaction and report generation module 5.

[0056] The AI ​​model building and adaptive learning module 3 is internally configured with an NLP text parsing unit 6, a feature engineering and feature storage unit 7, a multi-task learning model training unit 8, an incremental learning and model update unit 9, and a feedback integration and optimization unit 10.

[0057] The output end of the data acquisition and processing module 1 is connected to the input end of the job feature database module 2 and the raw data input end of the AI ​​model construction and adaptive learning module 3, respectively. The feature data output end of the job feature database module 2 is connected to the feature input interface of the AI ​​model construction and adaptive learning module 3 and the baseline data input end of the multivariate comprehensive evaluation module 4. The model parameter output end of the AI ​​model construction and adaptive learning module 3 is connected to the model configuration interface of the multivariate comprehensive evaluation module 4. The evaluation result output end of the multivariate comprehensive evaluation module 4 is connected to the data receiving end of the user interaction and report generation module 5. At the same time, the feedback data output end of the user interaction and report generation module 5 is connected back to the iterative optimization interface of the AI ​​model construction and adaptive learning module 3.

[0058] The data acquisition and processing module 1 is internally equipped with a multi-source data access interface, a data cleaning and standardization module, a structured conversion unit, a data quality monitoring component, a data desensitization and access control module, and a real-time data processing engine.

[0059] The Job Feature Database Module 2 includes a basic job information database, a skill tag system, a job template library, a dynamic feature update unit, a job association analysis tool, and a skill map construction unit.

[0060] NLP Text Parsing Unit 6 performs deep semantic extraction on unstructured text in human resources scenarios. Its core is achieved through a three-stage processing flow of "text preprocessing - semantic encoding - feature enhancement" to output structured features. First, in the text preprocessing stage, regular expressions are used to filter special symbols and stop words, and WordPiece word segmentation is used to divide the Chinese text into sub-word units. For multilingual scenarios in multinational enterprises, a multilingual BERT model is integrated to achieve unified encoding for 10 languages, including Chinese, English, Japanese, and Korean. Second, the semantic encoding layer, based on a pre-trained BERT-base model, performs context-aware encoding on the input text: key sentences in job descriptions such as "proficient in Python" and "5+ years of project management experience" are transformed into 768-dimensional vectors, and sentence vector pooling is used to generate the overall semantic representation of the text. To enhance professional domain adaptability, a domain data fine-tuning strategy is adopted: BERT is retrained using historical job data from the enterprise to optimize the semantic capture capabilities of "skill terminology" and "industry-specific expressions." Finally, the feature enhancement module achieves structured output through keyword extraction and semantic similarity calculation: it extracts the top 20 core keywords from the encoding results based on the TextRank algorithm, and links entities in conjunction with the industry skill tag library to generate standardized skill tags; at the same time, it calculates the matching degree between keyword vectors and job template vectors through cosine similarity to quantify the weight of each skill in the text.

[0061] The text preprocessing module of NLP text parsing unit 6 uses regular expressions to filter special symbols and stop words, and uses WordPiece word segmentation to divide Chinese text into sub-word units. For multilingual scenarios in multinational enterprises, it integrates a multilingual BERT model to achieve unified encoding for 10 languages, including Chinese, English, Japanese, and Korean. The semantic encoding layer, based on a pre-trained BERT-base model, performs context-aware encoding on the input text, transforming key sentences in job descriptions into 768-dimensional vectors, and generating an overall semantic representation of the text through sentence vector pooling. A domain data fine-tuning strategy is adopted, using historical job data from the enterprise to retrain BERT, optimizing the semantic capture capabilities of skill terminology and industry-specific expressions. The feature enhancement module extracts the Top 20 core keywords from the encoding results using the TextRank algorithm, combines them with an industry skill tag library for entity linking, and generates standardized skill tags. Cosine similarity is used to calculate the matching degree between keyword vectors and job template vectors, quantifying the weight of each skill in the text.

[0062] The dynamic weight fusion formula for NLP text parsing unit 6 is as follows:

[0063] ;

[0064] in:

[0065] w iThis represents the final weight of the i-th keyword (value range 0~1).

[0066] α represents the balance coefficient (value 0.3, determined through grid search optimization, used to adjust the contribution ratio of statistical features and semantic features).

[0067] TF-IDF i This represents the term frequency-inverse document frequency value of the i-th keyword (reflecting the statistical importance of the word in the text);

[0068] Attn i This represents the attention weight corresponding to the i-th keyword output by the BERT model (reflecting the importance of the word in the context semantics, with a value ranging from 0 to 1).

[0069] Sim i This represents the semantic similarity between the i-th keyword and the core skills in the job template library.

[0070] The feature engineering and feature storage unit 7 is internally equipped with a structured data processing module, a text feature conversion module, a feature fusion module, and a feature storage module.

[0071] The structured data processing module performs Z-Score standardization on numerical data such as employee performance scores, number of skill certificates, and number of project participations. For categorical data, it uses a combination of one-hot encoding and target encoding to preserve the ordered relationships between categories. The text feature conversion module compresses the semantic vectors output by the NLP parsing unit to 256 dimensions through principal component analysis and extracts Top-K key semantic units through attention pooling to generate a sparse binary feature matrix. The temporal feature extractor calculates the first and second differences of industry dynamic features based on a sliding window to capture trend features. The feature fusion module adopts a dynamic weighting mechanism, calculates the contribution of each feature to the model loss function through gradient boosting trees, and automatically adjusts the fusion ratio of structured features, text features, and temporal features. The feature storage module builds a hierarchical storage architecture based on a distributed columnar database. The hot data layer uses memory storage, and the cold data layer uses disk storage. It realizes time travel query through feature snapshots and integrates a feature version management system to record the iteration history of feature calculation logic.

[0072] The training unit 8 of the multi-task learning model is internally equipped with a shared encoder-task decoder architecture, a two-stage optimization strategy, a dynamic task weight mechanism, and an optimizer and learning rate scheduling module.

[0073] The shared encoder-task decoder architecture employs a 12-layer Transformer shared network at its bottom layer. It receives a 256-dimensional fused feature vector from the feature engineering unit and captures the deep correlations between job skills, industry trends, and employee capabilities through a self-attention mechanism. The upper layers consist of a job value assessment decoder, a skill matching decoder, and a performance potential prediction decoder, which output continuous scores, matching probabilities, and a potential index for the next 6 months, respectively. A two-stage optimization strategy is employed: the first stage involves pre-training on a general industry dataset to initialize the shared network parameters; the second stage integrates internal enterprise data and fine-tunes the task decoder through transfer learning, freezing the parameters of the bottom 6 Transformer layers to avoid overfitting. A dynamic task weight mechanism monitors the validation set loss of each task in real time, automatically increasing its weight when the loss fluctuation of a task exceeds a threshold. Task relevance regularization is introduced, using the Pearson correlation coefficient of task losses to share some decoder parameters for highly correlated tasks. The optimizer and learning rate scheduling module utilize the AdamW algorithm with weight decay.

[0074] The dynamic weight adjustment formula for training unit 8 of the multi-task learning model based on loss volatility and task relevance is as follows:

[0075] ;

[0076] in:

[0077] λ k The dynamic weight of the k-th task (job value assessment / skill matching degree / performance potential) is represented (value range 0~1).

[0078] This represents the reciprocal of the precision parameter for the k-th task (calculated using the Gaussian likelihood estimation of the task loss, reflecting the task difficulty; a larger value indicates a more difficult task, requiring a higher weight).

[0079] γ represents the volatility sensitivity coefficient (fixed at 0.8, controlling the intensity of the impact of loss volatility on the weights).

[0080] Var(L k ) represents the loss variance of the k-th task in the last 100 training rounds (reflecting loss stability; the larger the variance, the more the weights are reduced, avoiding the model being dominated by unstable tasks).

[0081] ρ k This represents the average correlation coefficient between the k-th task and other tasks (calculated using the Pearson correlation coefficient, with a value ranging from -1 to 1; positively correlated tasks are given higher weights to enhance the synergistic effect).

[0082] The multi-task learning model training unit 8's shared encoder-task decoder architecture employs a 12-layer Transformer shared network at its bottom layer. It receives a 256-dimensional fused feature vector from the feature engineering unit and captures the deep correlations between job skills, industry trends, and employee capabilities through a self-attention mechanism. The upper layers consist of a job value assessment decoder, a skill matching decoder, and a performance potential prediction decoder, outputting continuous scores, matching probabilities, and a potential index for the next 6 months, respectively. A two-stage optimization strategy is employed: the first stage involves pre-training on a general industry dataset to initialize the shared network parameters; the second stage integrates internal enterprise data, fine-tuning the task decoder through transfer learning and freezing the parameters of the bottom 6 Transformer layers to avoid overfitting. A dynamic task weight mechanism monitors the validation set loss of each task in real time, automatically increasing its weight when the loss fluctuation of a task exceeds a threshold. Task relevance regularization is introduced, calculating the Pearson correlation coefficient of task losses and sharing some decoder parameters for highly related tasks. The optimizer and learning rate scheduling module adopt the AdamW algorithm with weight decay. The learning rate scheduling strategy combines cosine annealing and a warm-up mechanism. The learning rate is linearly increased to the initial learning rate in the first 1000 steps, and then decayed according to the cosine curve.

[0083] The incremental learning and model update unit 9 is internally equipped with a data filtering and drift detection module, an incremental training and parameter protection module, a model compression module, and a version management module.

[0084] The incremental learning and model update unit 9's internal data filtering and drift detection module selects incremental training samples based on time windows and importance scores, removes outliers, and detects feature distribution drift using KL divergence. When the distribution difference between new data and historical data exceeds a threshold, it triggers a full feature recalculation; otherwise, it only updates the changed features. The incremental training and parameter protection module uses an elastic weight consolidation method, adding a secondary penalty term to important model parameters in historical tasks and introducing an adaptive learning rate mechanism. It sets a higher learning rate for parameters associated with drifting features and keeps the learning rate low for stable feature parameters. The model compression module uses knowledge distillation technology to transfer the knowledge of the updated large model to a lightweight model and uses quantization-aware training to compress the weights from 32-bit floating-point to 8-bit integer. The version management module automatically records parameter snapshots, data batches, and performance metrics for each update, supports one-click rollback to the best historical version, and generates a model health report that quantifies the degree of drift and update benefits.

[0085] The formula for the adaptive parameter protection strength of Incremental Learning and Model Update Unit 9 based on parameter importance and data drift is:

[0086] ;

[0087] in:

[0088] λp The protection strength for the p-th model parameter (value range 0~5, the larger the value, the more difficult it is for the parameter to be modified in incremental training);

[0089] α is the balance coefficient (fixed at 0.7, representing the proportion of the control parameter's contribution to stability).

[0090] I p The historical task importance of the p-th parameter (calculated using the EWC algorithm, reflecting the parameter's contribution to the accuracy of the historical task, with a value range of 0 to 1);

[0091] γ is the drift sensitivity coefficient (fixed at 0.5, adjusting the magnitude of the impact of data drift on the protection strength);

[0092] D p The drift of the p-th parameter associated feature (calculated by JS divergence to measure the difference in distribution between new and old data, with a value range of 0 to 1);

[0093] Var(θ p ) represents the variance of the p-th parameter in historical training (reflecting parameter stability; the smaller the variance, the more robust the parameter is, requiring higher protection strength; the value range is 0~1).

[0094] The feedback integration and optimization unit 10 is internally configured with a feedback collection module, a case analysis layer, a parameter tuning module, and a rule base update module.

[0095] The feedback collection module constructs a multi-source input interface, allowing HR administrators to annotate erroneous evaluation cases through the system interface, employees to submit skill self-assessments and objections to job matching via mobile devices, and industry experts to import the latest occupational standard data through an API interface. The case analysis layer uses density clustering algorithms to detect anomalies in the feedback data, identify high-frequency error types, locate the root cause of errors through a decision tree model, and distinguish between data quality issues and model defects. The parameter tuning module generates targeted training samples based on feedback cases, dynamically adjusts key parameters of each unit using reinforcement learning methods, and enhances model robustness by constructing edge cases through adversarial generative networks. The rule base update module transforms expert feedback into executable rules, adds a skill tag system for emerging positions, corrects the semantic boundaries of terminology, and ensures the traceability of rule iteration through version control. The effect verification stage adopts a shadow deployment mode, running the optimized model and the online model in parallel, and comparing the evaluation results through double-blind testing. When the F1 score of the new model is more than 5% higher than that of the old model for three consecutive weeks, the online switch is automatically triggered, and an optimization report is generated to record the improvement of key indicators.

[0096] The multi-dimensional comprehensive evaluation module 4 includes an evaluation index system, a weight allocation mechanism, an evaluation model engine, an evaluation result calibration component, an evaluation cycle management tool, and a cross-dimensional comparative analysis module.

[0097] The multi-dimensional comprehensive evaluation module 4 has an internal evaluation indicator system that includes multiple dimensions such as job matching, skill proficiency, performance potential, and organizational fit. Each dimension has a quantifiable sub-indicator weight allocation mechanism. The weight of each evaluation indicator is determined by combining the analytic hierarchy process (AHP) with expert scoring. It supports dynamic optimization of the evaluation model engine based on corporate strategy adjustments, integrates machine learning algorithms to perform multi-dimensional analysis of employee data, generates comprehensive evaluation scores, and uses a calibration component to correct deviations in evaluation results through historical data comparison and industry benchmark references, ensuring the objectivity and consistency of the evaluation. The evaluation cycle management tool supports configuring evaluation tasks by quarter, year, or project cycle, automatically triggering the evaluation process. The cross-dimensional comparison analysis module supports vertical comparison of evaluation results for the same employee in different cycles and horizontal comparison of employees in the same position, generating differential analysis reports.

[0098] The user interaction and report generation module 5 includes a visual operation interface, a report template library, a data export component, a feedback collection unit, a user permission management module, and an automated report scheduling component.

[0099] The user interaction and report generation module 5 features a visual interface that provides functions such as evaluation task creation, indicator configuration, and result viewing. It supports drag-and-drop operation and a customizable dashboard report template library, including various preset templates such as job evaluation reports, employee development reports, and talent inventory reports. It also supports user-defined report formats and a data export component, allowing evaluation results to be exported to multiple formats such as Excel and PDF. Furthermore, it provides a data interface for other systems to access the feedback collection unit, allowing users to annotate and provide feedback on evaluation results, collect improvement suggestions, and transmit feedback data to the system optimization module. The user permission management module, based on the RBAC model, configures the operation permissions for roles such as HR administrators, department heads, and employees, controlling the scope of data viewing and editing. The report automation scheduling component supports setting up scheduled report generation and pushing via email, WeChat Work, and other channels, and also supports report subscription functionality.

[0100] From the above, we can conclude that:

[0101] In this invention, through the model training unit of the AI ​​model construction and adaptive learning module, the system can integrate multi-source data for in-depth analysis, avoiding the limitations of traditional evaluation methods that rely on subjective experience. The adaptive learning engine captures changes in job requirements in real time, allowing evaluation criteria to be dynamically adjusted to ensure that the assessment results always align with the company's actual staffing needs. The algorithm optimization component continuously iterates on the underlying model, reducing the workload of manually adjusting parameters and improving the automation level of the evaluation process. This frees HR personnel from tedious data processing, allowing them to focus more on strategic talent management.

[0102] In this invention, the model evaluation tool periodically checks model performance, automatically triggering an optimization process upon detecting deviations to prevent distorted evaluation results due to model aging. The dynamic feature update unit continuously crawls job requirement data from external recruitment platforms, promptly incorporating emerging skill requirements into the evaluation system to help companies proactively plan talent development. The feedback collection unit continuously calibrates model parameters based on user feedback, making the system more aligned with HR operating habits and improving the tool's practicality and ease of use.

[0103] In this invention, the system enhances the overall efficiency of human resource management through the collaborative work of its various modules. The data acquisition and processing module's ability to integrate multi-source data makes the evaluation criteria more comprehensive and richer; the skill map constructed from the job characteristic database provides a visual analysis tool for talent assessment; and the cross-dimensional comparison function of the multi-dimensional comprehensive evaluation module helps managers quickly identify employees' strengths and weaknesses. The user interaction and report generation modules simplify the output process of evaluation results, and various automated reports allow decision-makers to intuitively grasp the talent situation, providing reliable support for human resource decisions such as recruitment, training, and promotion, and promoting the overall improvement of the enterprise's talent management level.

[0104] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A digital evaluation system for human resource positions based on big data and AI, characterized in that: It includes a data acquisition and processing module (1), a job feature database module (2), an AI model building and adaptive learning module (3), a multi-dimensional comprehensive evaluation module (4), and a user interaction and report generation module (5). The AI ​​model building and adaptive learning module (3) is internally equipped with an NLP text parsing unit (6), a feature engineering and feature storage unit (7), a multi-task learning model training unit (8), an incremental learning and model update unit (9), and a feedback integration and optimization unit (10). The output of the data acquisition and processing module (1) is connected to the input of the job feature database module (2) and the raw data input of the AI ​​model construction and adaptive learning module (3), respectively. The feature data output end of the job feature database module (2) is connected to the feature input interface of the AI ​​model construction and adaptive learning module (3) and the benchmark data input end of the multivariate comprehensive evaluation module (4); The model parameter output terminal of the AI ​​model construction and adaptive learning module (3) is connected to the model configuration interface of the multivariate comprehensive evaluation module (4); The evaluation result output end of the multi-dimensional comprehensive evaluation module (4) is connected to the data receiving end of the user interaction and report generation module (5); The feedback data output of the user interaction and report generation module (5) is connected back to the iterative optimization interface of the AI ​​model building and adaptive learning module (3).

2. The digital evaluation system for human resource positions based on big data and AI according to claim 1, characterized in that: The data acquisition and processing module (1) is internally equipped with a multi-source data access interface, a data cleaning and standardization module, a structured conversion unit, a data quality monitoring component, a data desensitization and access control module, and a real-time data processing engine.

3. The human resources job digital evaluation system based on big data and AI according to claim 1, characterized in that: The job feature database module (2) is internally equipped with a job basic information database, a skill tag system, a job template library, a dynamic feature update unit, a job association analysis tool, and a skill map construction unit.

4. The human resources job digital evaluation system based on big data and AI according to claim 1, characterized in that: The dynamic weight fusion formula of the NLP text parsing unit (6) is as follows: ； in: w i This represents the final weight of the i-th keyword; α represents the balance coefficient; TF-IDF i This represents the term frequency-inverse document frequency value of the i-th keyword; Attn i This represents the attention weight corresponding to the i-th keyword output by the BERT model; Sim i This represents the semantic similarity between the i-th keyword and the core skills in the job template library.

5. The human resources job digital evaluation system based on big data and AI according to claim 1, characterized in that: The feature engineering and feature storage unit (7) is internally equipped with a structured data processing module, a text feature conversion module, a feature fusion module and a feature storage module.

6. The digital evaluation system for human resource positions based on big data and AI according to claim 1, characterized in that: The multi-task learning model training unit (8) is internally equipped with a shared encoder-task decoder architecture, a two-stage optimization strategy, a dynamic task weight mechanism, and an optimizer and learning rate scheduling module. The dynamic weight adjustment formula for the multi-task learning model training unit (8) based on loss volatility and task relevance is as follows: ； in: This represents the dynamic weight of the k-th task; This represents the reciprocal of the precision parameter for the k-th task; γ represents the fluctuation sensitivity coefficient; This represents the variance of the loss of the k-th task in the most recent 100 training rounds; ρ k This represents the average correlation coefficient between the k-th task and other tasks.

7. The digital evaluation system for human resource positions based on big data and AI according to claim 1, characterized in that: The incremental learning and model update unit (9) is internally equipped with a data filtering and drift detection module, an incremental training and parameter protection module, a model compression module and a version management module; The adaptive parameter protection strength formula of the incremental learning and model update unit (9) based on parameter importance and data drift is as follows: ； in: λ p Let be the protection strength of the p-th model parameter; α is the balance coefficient; I p The historical task importance of the p-th parameter; γ is the drift sensitivity coefficient; D p Let be the drift degree of the feature associated with the p-th parameter; Var(θ p ) represents the variance of the p-th parameter during historical training.

8. The digital evaluation system for human resource positions based on big data and AI according to claim 1, characterized in that: The feedback integration and optimization unit (10) is internally equipped with a feedback acquisition module, a case analysis layer, a parameter tuning module, and a rule base update module.

9. A digital evaluation system for human resource positions based on big data and AI according to claim 1, characterized in that: The multi-dimensional comprehensive evaluation module (4) is internally equipped with an evaluation index system, a weight allocation mechanism, an evaluation model engine, an evaluation result calibration component, an evaluation cycle management tool, and a cross-dimensional comparison analysis module.

10. A digital evaluation system for human resource positions based on big data and AI according to claim 1, characterized in that: The user interaction and report generation module (5) is internally equipped with a visual operation interface, a report template library, a data export component, a feedback collection unit, a user permission management module, and a report automation scheduling component.

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