Personnel and post intelligent matching method and system based on multi-dimensional feature portrait

Abstract

The invention provides a personnel and post intelligent matching method and system based on a multi-dimensional feature portrait, and relates to the technical field of intelligent matching, and the method comprises the steps: collecting personnel occupational full-life-cycle multi-source data and post total-factor demand data, extracting semantic features, constructing a personnel-post association feature map, and forming a multi-dimensional feature evolution data set; core features are identified based on a deep learning algorithm, and dynamic weight coefficients of all feature dimensions are output; fitting strength coupling analysis is completed through an analytic hierarchy process, and fitting strength scores and dynamic matching trend data are obtained; training an optimal matching model by adopting an improved gradient boosting tree algorithm, outputting a candidate result and analyzing a matching contribution degree; continuously optimizing the model by comparing the actual behavior characteristics with the prediction characteristics; the system comprises a matching result verification and optimization module and the like, high-precision and multi-dimensional personnel and post matching is realized, and the efficiency and accuracy of human resource configuration are effectively improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent matching technology, and in particular to a method and system for intelligent matching of personnel and positions based on multi-dimensional feature profiles. Background Technology

[0002] In the current field of human resource recruitment, traditional methods of matching personnel with positions mainly rely on resume keyword screening or static rule matching, which are difficult to handle complex multidimensional features and dynamic changes. For example, in the recruitment scenarios of large technology companies, job requirements not only involve hard skills but also soft factors such as team culture fit and individual development potential. Existing technologies, such as keyword-based matching systems, often only focus on skill keywords in resumes and cannot effectively analyze unstructured data such as project descriptions or career plans. They also ignore dynamic dimensions such as cultural fit, leading to inaccurate matching results. Specifically, new employees leave quickly after joining the company due to cultural incompatibility or insufficient development space, resulting in high recruitment costs. For example, when a company recruited software engineers, although it matched qualified candidates, it ignored the conflict between their collaboration preferences and the team's innovative culture, resulting in a high turnover rate within three months of joining. Existing technologies lack quantitative analysis of the temporal evolution of personnel characteristics and the dynamic adjustment of job requirements, and it is also difficult to verify the matching effect through actual behavioral data. Therefore, there is an urgent need for a new intelligent matching method and system for personnel and positions based on multidimensional feature profiles. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for intelligent matching of personnel and positions based on multi-dimensional feature profiles. This addresses the problems in existing technologies, such as low matching accuracy due to reliance on static, single-dimensional keyword matching, neglect of dynamic evolutionary features and deep semantic information of personnel and positions, and lack of effective verification and model self-optimization mechanisms based on actual onboarding performance. These problems result in low recruitment efficiency, poor person-position matching, and high employee turnover. The specific technical solution is as follows: This invention provides a method for intelligent matching of personnel and positions based on multi-dimensional feature profiling, including: Step 1: Collect multi-source data on the entire career life cycle of personnel and full-element requirement data of positions. Perform noise reduction and standardization processing on the multi-source data and position data. Use the BERT model to extract the semantic features of unstructured data. Combine the quantitative features of structured data to construct a personnel-position association feature map. Extract the temporal evolution trajectory of personnel features and the dynamic change features of position requirements to form a multi-dimensional feature evolution dataset. Step 2: Based on the multidimensional feature evolution dataset, use deep learning algorithms to identify the core competency features of personnel and the core requirements features of positions, introduce an attention mechanism to construct a dynamic weight model, and combine historical matching cases to train and output the dynamic weight coefficients of each feature dimension under different job types. Step 3: Construct a measurement model using the analytic hierarchy process (AHP), and combine it with the dynamic weight coefficients to quantify skill fit, cultural fit, and development potential fit. Integrate job priority and employee career development aspirations to complete the fit strength coupling analysis, and obtain fit strength scores and dynamic matching trend data. Step 4: Train the dynamic matching trend data based on the improved gradient boosting tree algorithm to obtain the optimal matching model and deploy it. Real-time data interaction is used to achieve dynamic intelligent matching. Combinations with a matching degree higher than a preset threshold are selected as candidate results. If the number of candidate results does not reach the preset number, supplementary data collection and secondary matching are triggered. The candidate results and matching contribution analysis of each dimension are output.

[0004] Furthermore, it also includes steps for verifying and optimizing the preliminary matching results, including: Step 40: Define the candidate results as expected matching results; Step 41: Based on the expected matching results, collect the matched personnel behavior data and job performance data; Step 42: Based on the personnel behavior data and job performance data, extract confirmatory behavioral features; Step 43: Compare and analyze the confirmatory behavioral features with the matching features on which the expected matching results are based; Step 44: When the matching degree between the verification behavior feature and the matching feature is higher than the preset verification threshold, the expected matching result is determined as the target matching result and output. Step 45: When the matching degree between the confirmatory behavioral feature and the matching feature is lower than the preset verification threshold, output the model optimization signal and define the relevant data pairs as feedback training samples.

[0005] Furthermore, when outputting the model optimization signal, the process also includes deep processing and model iteration steps, specifically including: Step 450: Collect the feedback training samples into a preset low-confidence matching queue and accumulate the number of samples; Step 451: When the number of samples reaches the preset reliable number, execute the K-means clustering algorithm to analyze the common deviations of the samples; Step 452: Determine the dimensions of the bias characteristics and the types of bias sources based on the cluster analysis results; Step 453: Execute the corresponding correction strategy on the feedback training samples according to the type of deviation source to generate a corrected matching result; Step 454: Based on the corrected matching result, trigger a new round of short-term behavior tracking and collect verification data. When the verification is successful, output the corrected result and update the optimal matching model. When the verification fails, output the model structure defect signal. Step 455: In response to the model structure defect signal, select a basic model architecture based on the preset model library and train it with the feedback training samples to obtain the model to be verified, and evaluate its performance through cross-validation. Step 456: When cross-validation passes, deploy the model to be validated in the shadow system for low-volume A / B testing. After the test passes, define it as an iterative matching model and add it to the set of running models. Step 457: When all the cross-validation of the preset model architectures fails, the self-learning modeling process is triggered. A self-learning model prototype is generated based on the common patterns of the subclusters in the feedback training samples. After training and validation, it is added to the model library.

[0006] Furthermore, it also includes steps for deep feature compensation and expert intervention after the model structure defect signal is triggered, specifically including: Step 4800: Based on the feedback training samples corresponding to the model structure defect signal, query the acceptable range of the corresponding deviation feature dimension in the preset talent feature standard distribution table as the feature compensation threshold. Step 4801: Based on the original feature data in the feedback training samples and the feature compensation threshold, determine the outlier feature samples and the feature items to be compensated; Step 4802: Based on the preset generative adversarial network model, generate simulated feature values ​​that meet the feature compensation threshold and are logically consistent with other features of the sample for the feature items to be compensated, replace the original values ​​to form a corrected feature vector, and input the corrected feature vector into the model to be verified for verification. Step 4803: After all the features to be compensated have been processed and the model has been validated, the rule for generating simulated feature values ​​is defined as a virtual matching rule and stored in the model-aided knowledge base; Step 4804: When there are features to be compensated that cannot be generated logically consistent simulated feature values ​​through the generative adversarial network model, output an unresolvable feature signal.

[0007] Furthermore, it also includes a method for expert system intervention in response to the unresolvable feature signal, comprising: Step 4805: Based on the sample that triggered the unresolvable signal of the feature, query the preset human resources expert tag library to determine the category of difficult cases to which the sample belongs and the suggested handling expert role; Step 4806: Process expert roles according to the recommendations, find the corresponding online expert system or human expert interface, and push case summaries and feature unresolved alerts; Step 4807: Receive feature correction instructions and rule definitions from expert systems or human experts, generate expert correction rules and store them in the model-assisted knowledge base, and update the feedback training sample set using the corrected samples. Step 4808: Based on the updated feedback training sample set, re-trigger the self-learning modeling process, and store the newly generated model prototype in the model library after associating and labeling it with the expert correction rules.

[0008] Furthermore, it also includes a handling method in response to continuous matching failures, including: Step 4809: When the number of unresolved characteristic signals received by the same job or similar job within a preset time window exceeds a preset threshold, it is determined that the target job requirement profile is abnormal, and a job profile revision suggestion is sent to the job publisher. Step 4810: Receive the job posting data updated by the job poster based on the revision suggestions, replace the original job requirements data, re-execute the full process matching from Step 1 to Step 4, and associate and store the update record with subsequent successful matching cases.

[0009] Furthermore, the multi-source data on the entire career lifecycle of personnel mentioned in step 1 includes career history, skills certification, competency assessment, and career planning data; the full-element job requirement data includes job descriptions, skills requirements, organizational culture adaptation requirements, and job workload thresholds; the noise reduction and standardization process includes eliminating outliers through interquartile range detection and Z-score standardization, and filtering invalid characters using regular expressions; when extracting semantic features from unstructured data using the BERT model, the text description is input into a pre-trained language model to generate a high-dimensional vector representation to capture deep semantic information; the construction of the personnel-job association feature map is to associate personnel attributes with job requirements through a graph structure, where nodes represent entities such as skills or experience, and edges represent the strength of the association; the deep learning algorithm mentioned in step 2 uses convolutional neural networks or recurrent neural networks to evolve... The dataset automatically learns salient patterns. In the historical matching cases, positive samples are selected from matching records retained for more than 3 months after joining the company, and negative samples are selected from records with high matching degree but rejected or short-term departure. In step 2, when training and outputting dynamic weight coefficients, time decay weighted sampling technology is used to make the model prioritize learning recent market preferences. In step 3, the skill fit is obtained by calculating the cosine similarity between the personnel skill vector and the job requirement vector, while considering both hard skill matching degree and soft skill semantic similarity. The cultural fit is calculated based on the overlap between organizational culture characteristics and individual work preferences. The development potential fit is quantified by analyzing the consistency between personnel learning trajectory and job promotion path. In step 3, the fit strength coupling analysis uses a weighted summation model to fuse multi-dimensional scores, and the fit strength score is a normalized comprehensive value, ranging from 0 to 1.

[0010] Furthermore, the improved gradient boosting tree algorithm described in step 4 is an optimized version based on the traditional GBDT, which introduces a regularization term and an early stopping mechanism. When training the optimal matching model, the fit strength score is used as the target variable, multidimensional features are used as input features, and cross-validation is used to select the best hyperparameters. The real-time data interaction described in step 4 achieves efficient similarity retrieval through vector database technology, and uses LanceDB's IVF-PQ index to accelerate the query. The matching contribution analysis described in step 4 uses Shapley value decomposition to decompose the marginal impact of each feature on the final score.

[0011] This invention also provides an intelligent matching system for personnel and positions based on multi-dimensional feature profiling, used to implement the method, including: The multi-source data acquisition and feature map construction module is used to collect multi-source data on the entire career life cycle of personnel and full-element requirement data of positions. After denoising and standardizing the two types of data, the BERT model is used to extract the semantic features of unstructured data. Combined with the quantitative features of structured data, a personnel-position association feature map is constructed to extract the temporal evolution trajectory of personnel features and the dynamic change features of position requirements, forming a multi-dimensional feature evolution dataset. The dynamic weight model training module is used to identify the core competency features of personnel and the core requirement features of positions based on the evolutionary dataset using deep learning algorithms, introduce an attention mechanism to construct a dynamic weight model, and combine historical matching cases to train and output dynamic weight coefficients for each feature dimension under different job types. The Adaptation Strength Coupling Analysis Module is used to construct a measurement model through the Analytic Hierarchy Process (AHP), combine the dynamic weight coefficients to quantify skill adaptability, cultural adaptability, and development potential fit, integrate job priority and personnel career development aspirations to complete the adaptation strength coupling analysis, and obtain adaptation strength scores and dynamic matching trend data. The intelligent matching and result generation module is used to train the trend data based on the improved gradient boosting tree algorithm to obtain the optimal matching model and deploy it. It realizes dynamic intelligent matching through real-time data interaction, filters combinations with a matching degree higher than a preset threshold as candidate results, and triggers supplementary data collection and secondary matching if the preset number is not reached. It outputs candidate results and analysis of matching contribution in each dimension. The matching result verification and optimization module is used to verify and optimize the preliminary matching results. This includes collecting matched personnel behavior data and job performance data, extracting confirmatory behavioral features, comparing and analyzing them with matching features, and determining the target matching result or triggering a model optimization signal based on the verification results.

[0012] Furthermore, the multi-source data acquisition and feature map construction module specifically includes: The data acquisition and standardization unit is used to collect multi-source data on the entire career life cycle of personnel and full-element requirement data of job positions. Outliers are eliminated through interquartile range detection and Z-score standardization, and invalid characters are filtered using regular expressions. The semantic feature extraction unit is used to input text descriptions into a pre-trained BERT model and generate high-dimensional vector representations to extract semantic features from unstructured data. The feature graph construction unit is used to associate personnel attributes with job requirements through a graph structure. Nodes represent entities such as skills or experience, and edges represent the strength of association, thus constructing a personnel-job association feature graph. The temporal evolution analysis unit is used to analyze the characteristic change patterns in personnel historical data, extract the temporal evolution trajectory of personnel characteristics and the dynamic change characteristics of job requirements; The evolutionary dataset generation unit is used to integrate all processed features to form a standardized multidimensional feature evolutionary dataset; The dynamic weight model training module specifically includes: The core feature recognition unit is used to automatically learn the core competency features of personnel and the core requirement features of positions from evolutionary datasets using convolutional neural networks or recurrent neural networks; The attention mechanism unit is used to dynamically evaluate the impact of different features on the matching results and assign higher weights to key features such as scarce skills. The weighted model training unit is used to train a dynamic weighted model based on historical successful matching cases using the random forest algorithm, and employs time decay weighted sampling technology to enable the model to learn recent market preferences first. The weight coefficient output unit is used to output the dynamic weight coefficients of each feature dimension under different job types.

[0013] A computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the method.

[0014] The beneficial effects of this invention are as follows: Addressing the current problem of mismatch between recruits and employees after onboarding, traditional methods can only statically match resume keywords and job descriptions, easily leading to short-term employee turnover due to cultural conflicts. This invention constructs a multi-dimensional dynamic profile integrating skills, culture, and development potential. It not only deeply analyzes unstructured text such as project experience to identify genuine work styles, but also utilizes historical matching data and post-employment behavioral feedback for closed-loop verification and model self-optimization. This allows the system to accurately identify candidates suitable for in-depth technical architecture research and match them with R&D positions focused on technical breakthroughs, thereby reducing the risk of situational turnover and achieving an improvement from "resume matching" to "person-job fit."

[0015] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0016] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a schematic diagram illustrating the steps of the intelligent matching method between personnel and positions based on multi-dimensional feature profiling in this invention; Figure 2 This is a schematic diagram of the structure of the intelligent matching system for personnel and positions based on multi-dimensional feature profiling of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] In embodiments of the present invention, a method for intelligent matching of personnel and positions based on multi-dimensional feature profiling is provided. Please refer to [link to relevant documentation]. Figure 1 The method includes the following steps: This invention discloses an intelligent matching method for personnel and job positions based on multi-dimensional feature profiling. (Refer to...) Figure 1 A method for intelligent matching of personnel and positions based on multi-dimensional feature profiles includes: Step 1: Collect multi-source data on the entire career lifecycle of personnel and full-element job requirements data. The multi-source data includes career history, skills certification, competency assessment and career planning data. The job data includes job descriptions, skills requirements, organizational culture adaptation requirements and job workload thresholds. After denoising and standardizing both types of data, the BERT model is used to extract semantic features of unstructured data. Combined with the quantitative features of structured data, a personnel-job association feature map is constructed. The temporal evolution trajectory of personnel features and the dynamic change features of job requirements are extracted to form a multi-dimensional feature evolution dataset.

[0019] Multi-source data covering the entire career lifecycle of an employee refers to a comprehensive collection of information gathered from job seekers' career development, including but not limited to work experience, educational background, skills certificates, competency assessment results, and career development plans. Job requirement data refers to the comprehensive requirements proposed by employers for specific positions, covering job descriptions, necessary skills, team culture fit, and workload limitations. Data denoising and standardization refers to the process of eliminating outliers through interquartile range detection and Z-score standardization, and filtering invalid characters using regular expressions. When using the BERT model to extract semantic features from unstructured data, the text description is input into a pre-trained language model to generate a high-dimensional vector representation to capture deep semantic information. Constructing an employee-job association feature map involves associating employee attributes with job requirements through a graph structure, where nodes represent entities such as skills or experience, and edges represent the strength of the association. Extracting temporal evolution trajectories involves analyzing the patterns of feature changes in historical employee data, such as skill improvement paths or job expansion trends. Dynamic job requirement changes are obtained by monitoring the update frequency and content adjustments of recruitment requirements. The multi-dimensional feature evolution dataset is a standardized dataset that integrates all processed features for subsequent model training.

[0020] Step 2: Based on the evolutionary dataset, use deep learning algorithms to identify the core competency features of personnel and the core requirements features of positions, introduce an attention mechanism to construct a dynamic weight model, and combine historical matching cases to train and output the dynamic weight coefficients of each feature dimension under different job types.

[0021] Deep learning algorithms refer to techniques that use convolutional neural networks or recurrent neural networks to automatically learn salient patterns from evolutionary datasets. Identifying core competency features involves analyzing key discriminative factors in personnel data using neural networks, such as high-frequency skills or outstanding project experience. Core requirement features are essential qualifications extracted from job data, such as industry certifications or proficiency in specific tools. Attention mechanisms are computational resource allocation strategies that dynamically evaluate the impact of different features on matching results, for example, assigning higher weights to scarce skills. Dynamic weight models are weight allocation systems trained using the random forest algorithm based on historical successful matches; this model can adaptively adjust as market data changes. During training, positive samples are selected from matching records retained for more than 3 months after employment, while negative samples are selected from records of high-match scores but rejection or short-term departures. When outputting dynamic weight coefficients, time-decay weighted sampling is used to prioritize learning recent market preferences.

[0022] Step 3: Construct a measurement model using the analytic hierarchy process (AHP), combine dynamic weight coefficients to quantify skill fit, cultural fit, and development potential fit, integrate job priority with personnel career development aspirations to complete the fit strength coupling analysis, and obtain fit strength scores and dynamic matching trend data.

[0023] The Analytic Hierarchy Process (AHP) is a systematic analysis method that decomposes complex matching problems into target, criterion, and alternative layers. Skill fit is obtained by calculating the cosine similarity between the personnel's skill vector and the job requirement vector, considering both hard skill matching and soft skill semantic similarity. Cultural fit is calculated based on the overlap between organizational culture characteristics and individual work preferences, such as the degree of matching in collaboration patterns or innovation tendencies. Development potential fit is quantified by analyzing the alignment between the personnel's learning trajectory and their career advancement path. Fit strength coupling analysis is a trade-off process integrating job urgency and personnel career goals, for example, using a weighted summation model to fuse multi-dimensional scores. The fit strength score is a normalized composite value ranging from 0 to 1; a higher score indicates a better match quality. Dynamic matching trend data is a predictive indicator derived from analyzing historical score changes through a sliding time window, used to identify the evolution direction of the matching probability.

[0024] Step 4: Train the trend data based on the improved gradient boosting tree algorithm to obtain the optimal matching model and deploy it. Real-time data interaction is used to achieve dynamic intelligent matching. Combinations with a matching degree higher than a preset threshold are selected as candidate results. If the preset number is not reached, supplementary data collection and secondary matching are triggered. The candidate results and matching contribution analysis of each dimension are output.

[0025] The improved gradient boosting tree algorithm is an optimized version of the traditional GBDT algorithm, incorporating regularization terms and an early stopping mechanism. It reduces overfitting risk through iterative decision tree construction. When training the optimal matching model, the fit strength score is used as the target variable, multidimensional features as input features, and cross-validation is employed to select the optimal hyperparameters. The deployment process includes encapsulating the model as an API service and integrating it into the recruitment platform's workflow. Real-time data interaction refers to the system continuously receiving updated data on new job postings and personnel, triggering instant matching calculations. Dynamic intelligent matching achieves efficient similarity retrieval through vector database technology, such as using LanceDB's IVF-PQ index to accelerate queries. Preset thresholds are set by HR experts based on recruitment strategies, typically selecting the top 10% of candidates based on matching degree ranking. A supplementary data collection mechanism is activated when the number of candidates is insufficient, performing secondary matching by expanding skill keywords or relaxing experience requirements. Matching contribution analysis uses Shapley values ​​to decompose the marginal impact of each feature on the final score, visually representing key decision factors.

[0026] The above steps enable high-precision, multi-dimensional intelligent matching of personnel and positions, effectively improving the efficiency and accuracy of human resource allocation.

[0027] The intelligent matching method for personnel and positions based on multi-dimensional feature profiling also includes steps for verifying and optimizing the preliminary matching results, specifically including: Step 40: Define the candidate results as expected matching results before outputting them and applying them to the actual recruitment scenario.

[0028] The expected matching result refers to the preliminary person-job matching combination calculated based on the optimal matching model, which has not yet been verified by actual recruitment results. The main purpose of this definition is to provide an operational object for the subsequent verification and optimization process based on feedback from real scenarios.

[0029] Step 41: Based on the expected matching results, collect the matched personnel behavior data and job performance data.

[0030] Personnel behavior data refers to quantifiable behavioral records generated by candidates after they receive an interview or employment opportunity through the expected matching results, during the interview process, probationary period, and during their employment. This includes, but is not limited to, interview evaluation scores, skills test scores, probationary period project completion rate, training participation, and performance evaluation results. Job performance data refers to dynamic performance indicators related to the matched job, including the recruitment cycle length, interview pass rate, probationary period employee retention rate, and hiring department satisfaction rating. The system extracts corresponding time-series behavioral and performance data from the human resources information system and performance management system by associating personnel identifiers and job identifiers in the expected matching results.

[0031] Step 42: Extract confirmatory behavioral features based on the personnel behavior data and job performance data.

[0032] Confirmatory behavioral characteristics refer to key indicators extracted from actual collected data that reflect the true competence of individuals and the effectiveness of job matching. The system aggregates and calculates behavioral and performance data to extract multidimensional characteristics, including skill conversion efficiency, cultural integration speed, performance achievement cycle, and stability index. For example, skill conversion efficiency is quantified by comparing changes in the completion time of key skill tasks before and after onboarding; cultural integration speed is calculated by analyzing the frequency and positive feedback growth rate of individuals' participation in team collaboration activities within the first three months.

[0033] Step 43: Compare and analyze the confirmatory behavioral features with the matching features on which the expected matching result was generated.

[0034] Matching features refer to the original and derived features used in step 3 to calculate the fit strength score, such as the skill fit and cultural fit metrics on which the model is based. Comparative analysis refers to calculating the deviation and consistency indices between confirmatory behavioral features and their corresponding matching features. Specifically, the system constructs a feature comparison matrix, calculates the correlation between the predicted value of "skill fit" and the actual observed "skill conversion efficiency," and performs trend consistency analysis between the predicted value of "cultural fit" and the actual observed "cultural integration speed."

[0035] Step 44: When the matching degree between the confirmatory behavioral feature and the matching feature is higher than the preset verification threshold, the expected matching result is determined as the target matching result and output to the recruitment decision system.

[0036] The target matching result refers to a high-quality person-job matching combination that has been verified and confirmed as effective through real-world data. The preset verification threshold is derived from the data analysis of historical successful matching cases. For example, it requires a correlation coefficient of greater than 0.7 in the skill dimension and a trend consistency of greater than 80% in the cultural dimension. When the comparative analysis results meet the threshold requirements, it indicates that the model prediction is accurate and the matching result is reliable. The system then defines the matching as the target matching result and directly outputs it to the company's recruitment decision-making process or talent pool as the core basis for formal recruitment.

[0037] Step 45: When the matching degree between the confirmatory behavioral features and the matching features is lower than the preset verification threshold, output the model optimization signal and define the relevant data pairs as feedback training samples.

[0038] Model optimization signals are instructions triggered by the system during the validation process when a significant deviation is found between the model's predictions and the actual situation, indicating the need to retrain the optimal matching model. Feedback training samples are data pairs consisting of predicted mismatches, their corresponding original features, and actual collected validation behavioral features. The system stores these data pairs in a dedicated feedback sample library and triggers the model's incremental learning process. The optimal matching model will use the feedback training samples for a new round of training to adjust its internal parameters, thereby reducing similar prediction biases in subsequent matches and achieving self-iteration and continuous optimization of the model.

[0039] The intelligent matching method for personnel and positions based on multi-dimensional feature profiling also includes deep processing and model iteration steps after the model optimization signal is triggered, specifically including: Step 450: Collect the feedback training samples output from Step 45 into a preset low-confidence matching queue and accumulate the number of samples.

[0040] A low-confidence matching queue is a data structure used to store matching cases where the predicted results deviate significantly from the actual verification, following a first-in, first-out (FIFO) principle. Feedback training samples refer to matching data pairs where the match between the confirmatory behavioral features and the matching features is below a preset verification threshold. The sample size refers to the total number of feedback training samples stored in the low-confidence matching queue. The cumulative sample size ensures sufficient data to support subsequent statistical analysis and avoids unreliable conclusions due to insufficient samples.

[0041] Step 451: When the number of samples reaches the preset reliable number, execute the preset K-means clustering algorithm to analyze the common deviations of the samples.

[0042] The reliable number is a threshold set by the system for the number of samples to initiate in-depth analysis; its value is determined based on statistical significance requirements. Common bias refers to a consistent pattern of prediction errors across multiple feedback training samples on a specific feature dimension. K-means clustering is an unsupervised machine learning algorithm that iteratively divides data samples into K clusters, maximizing similarity within the same cluster and minimizing similarity between different clusters. When the number of samples reaches the reliable number, the system initiates K-means clustering to perform cluster analysis on the prediction error vectors in all feedback training samples, identifying systematic and common types of bias, such as a general overestimation of skill dimensions or a general underestimation of cultural dimensions.

[0043] Step 452: Determine the dimensions of the deviation features and the types of deviation sources based on the cluster analysis results.

[0044] Bias feature dimensions refer to specific feature categories where predicted and actual values ​​differ significantly, such as skill fit, cultural fit, or development potential alignment. Bias source types are categorized based on the root cause distinguished by error patterns, primarily including feature quantification error and weight allocation error. Feature quantification error refers to inaccurate extraction and quantification of original personnel characteristics or job requirement characteristics. Weight allocation error refers to unreasonable dynamic weight coefficients assigned to different feature dimensions by the model. The system identifies the most significant bias feature dimensions by analyzing the error vector represented by the centroid of each cluster, and further uses error decomposition techniques to determine whether the bias primarily originates from the feature quantification process or the weight allocation process.

[0045] Step 453: Execute the corresponding correction strategy on the feedback training samples according to the type of bias source to generate corrected matching results.

[0046] Corrected matching results refer to the new matching data obtained after adjusting the original low-confidence matching results. If the source of bias is determined to be feature quantization error, the correction strategy is to reverse-calibrate the quantization values ​​of the corresponding dimensions in the personnel feature vector and the job requirement vector based on the actual confirmatory behavioral characteristics of the batch of samples. Specifically, quantile standardization is used to map the quantization values ​​of feature dimensions with large prediction bias to the distribution range of actual observations. If the source of bias is determined to be weight allocation error, the correction strategy is to keep the original feature vector unchanged and adjust the weight coefficients corresponding to the biased feature dimension in the dynamic weight model trained in step 2. The adjustment magnitude is proportional to the average bias of that dimension in the cluster. After applying the correction strategy, the adaptation strength score is recalculated using the updated feature vector or weights to generate corrected matching results.

[0047] Step 454: Based on the corrected matching results, trigger a new round of short-term behavior tracking and collect verification data. When the verification is successful, output the corrected results and update the optimal matching model. When the verification fails, output the model structure defect signal.

[0048] The new round of short-term behavior tracking refers to the process of re-collecting behavioral and performance data for individuals in the corrected matching results during subsequent interviews or short-term trial periods. Validation data refers to the newly collected confirmatory behavioral features. The system compares the new validation data corresponding to the corrected matching results with the corrected predicted features. If the matching degree is higher than the preset corrected validation threshold, the validation is considered successful. The system then outputs the corrected matching result to the decision system and simultaneously uses this batch of successful corrected samples to incrementally learn the optimal matching model and update the model parameters. If the matching degree is still lower than the corrected validation threshold, the validation is considered unsuccessful, and a model structure defect signal is output. This signal indicates that the current model's basic algorithm structure may not be able to effectively learn this type of matching pattern, requiring a deeper model iteration process.

[0049] Step 455: In response to the model structure defect signal, select a basic model architecture based on the preset model library and train it with feedback training samples to obtain the model to be verified, and evaluate its performance through cross-validation.

[0050] The preset model library is a collection of various machine learning model architecture templates, including but not limited to different deep neural network structures, support vector machines, and ensemble learning models. A basic model architecture is a model template selected sequentially from the model library. The model to be validated is a new model retrained using all feedback training samples according to the selected basic model architecture. Cross-validation is a method that divides the training data into K parts, using one part as the validation set and the rest as the training set to evaluate the model's generalization performance. After training the model to be validated, the system uses K-fold cross-validation to calculate its average performance index. If the average performance index exceeds a preset model performance threshold, proceed to steps 4, 5, and 6; otherwise, select the next basic model architecture from the model library and repeat this step until a qualified model is found or all preset models are traversed.

[0051] Step 456: When cross-validation passes, deploy the model to be validated to the shadow system for low-volume A / B testing, and after the test passes, define it as an iterative matching model and add it to the set of running models.

[0052] The shadow system is a parallel system operating alongside the main online system without impacting actual decision-making; it's used for security testing of new models. Small-scale A / B testing involves diverting a small number of real-time matching requests to the new model and comparing the results with the original optimal matching model. The test passes when the new model's matching results on the diverted requests show a short-term pass rate significantly higher than or no lower than the original model. The iterative matching model refers to the updated model that has passed the above verification and is ready for practical use. The running model set is the collection of all available and verified matching models in the system. The system adds iterative matching models to this set, allowing subsequent matching tasks to select a model from this set based on the scenario, thus achieving model iteration and coexistence.

[0053] Step 457: When all the cross-validation of the preset model architecture fails, the self-learning modeling process is triggered. A self-learning model prototype is generated based on the common patterns of subclusters in the feedback training samples. After training and validation, it is added to the model library.

[0054] The self-learning modeling process refers to the process by which the system automatically summarizes feature interaction patterns from the data and constructs a new model structure when the preset model library is ineffective. The sub-cluster common pattern refers to the stable features and error relationship exhibited by the sample cluster with the highest internal consistency obtained after the clustering analysis in step 451. Based on the samples of this sub-cluster, the system extracts the nonlinear interaction rules between its high-contribution features and defines a new model prototype structure accordingly. The self-learning model prototype is the initial model framework constructed based on these rules. The system trains this prototype using all the data from this sub-cluster and validates it using data from the remaining clusters. If the validation is successful, this prototype and its optimal trained parameters are used as a new basic model architecture and stored in the preset model library, thereby expanding the system's model selection range and realizing the model's self-evolution.

[0055] The method for intelligent matching of personnel and positions based on multi-dimensional feature profiling also includes steps of deep feature compensation and expert intervention after the model structure defect signal is triggered, specifically including: Step 4800: Based on the feedback training samples corresponding to the model structure defect signals, query the acceptable range of the corresponding deviation feature dimension in the preset talent feature standard distribution table and use it as the feature compensation threshold.

[0056] The Talent Characteristic Standard Distribution Table stores the statistical distribution range of key characteristics of personnel across various industries and job types. These characteristics include the mean, standard deviation, and typical fluctuation range for dimensions such as skill mastery, cultural orientation score, and historical performance level. The acceptable range refers to the normal value range for the corresponding deviation characteristic dimension found in this table. The feature compensation threshold is a boundary value set based on this acceptable range to determine whether a sample feature value is abnormal. When a model structure defect signal is triggered, indicating that the existing model architecture cannot effectively learn this matching pattern, the system will automatically query this table to obtain the historical statistical distribution range corresponding to the current deviation characteristic dimension, serving as the basis for subsequent feature compensation operations.

[0057] Step 4801: Based on the original feature data and feature compensation threshold in the feedback training samples, determine the outlier feature samples and the feature items to be compensated.

[0058] The original feature data refers to the original feature vectors of personnel and positions input into the optimal matching model when generating this batch of incorrect matching results. Outlier feature samples are those where at least one feature dimension exceeds the feature compensation threshold. The feature terms to be compensated refer to the specific feature dimensions and their values ​​that exceed the threshold in the outlier feature samples. The system compares the original feature vector of each feedback training sample with the corresponding feature compensation threshold, filters out samples with feature values ​​exceeding the acceptable range, and records the exceeding feature terms and their values.

[0059] Step 4802: Based on the preset generative adversarial network model, generate simulated feature values ​​that meet the feature compensation threshold and are logically consistent with other features of the sample for the feature terms to be compensated, replace the original values ​​to form a corrected feature vector, and input the vector into the model to be verified selected in step 455 for verification.

[0060] The Generative Adversarial Network (GAN) model comprises a generator and a discriminator, trained to generate synthetic data consistent with the distribution of real-world data. For each feature to be compensated, the system utilizes the generator of the GAN model to generate a new feature value, conditioned on other normal feature values ​​of the sample. This new feature value must meet two conditions: first, its value must fall within the acceptable range specified by the feature compensation threshold; second, it must maintain consistency with other features in the sample in terms of business logic. For example, if a sample's "Advanced Java Programming" skill is rated abnormally high, while its "Large Project Experience" feature value is very low, the generated new skill score should be lowered within a reasonable range to match the level of project experience. After replacing the original outlier value with the generated simulated feature value, a corrected feature vector is formed. The system inputs this corrected feature vector into the model being evaluated in step 455, recalculates the prediction results, and performs cross-validation to assess whether feature compensation improves the model's prediction accuracy on this sample.

[0061] Step 4803: After all the features to be compensated have been processed and the model has been verified, the rule for generating simulated feature values ​​is defined as a virtual matching rule and stored in the model-aided knowledge base.

[0062] A virtual matching rule refers to the conditional mapping relationship learned by the generative adversarial network model during the feature compensation process, which generates reasonable feature values ​​from outlier feature values. It exists in the form of model parameters or a decision tree rule set. The model-aided knowledge base is a database used to store empirical or transformation rules that cannot be directly learned by the main model but are beneficial for handling marginal cases. When feature compensation for all outlier samples is completed, and the model to be validated, trained using the corrected feature vectors, performs satisfactorily on the validation set, the system extracts and encapsulates the core mapping relationship learned by the generator during this compensation process, storing it as a virtual matching rule in the model-aided knowledge base. In future matching processes, if similar outlier features are encountered again, this rule can be used preferentially for preprocessing.

[0063] Step 4804: When there are features to be compensated that cannot generate logically consistent simulated feature values ​​through the generative adversarial network model, output a preset feature unresolvable signal.

[0064] A feature unresolvable signal is an instruction issued by the system when it determines that a certain outlier feature value is too abnormal or contradictory, and cannot generate a reasonable alternative value based on the existing data distribution and logical relationships. This signal indicates that the automatic feature compensation mechanism has failed and more advanced intervention is required.

[0065] This also includes a method for expert system intervention in response to the unresolvable feature signal, the method comprising: Step 4805: Based on the samples with unresolvable trigger feature signals, query the preset human resources expert tag library to determine the category of difficult cases to which the samples belong and the suggested handling expert role.

[0066] The HR expert tag library is a database that stores various categories of complex, special, or historically challenging matching cases and corresponding expert recommendations for handling them. The categories of challenging cases are defined based on case characteristics, such as "cross-domain transformation assessment" and "scarce skills combination assessment." Expert roles for handling recommendations refer to the identities of experts with expertise in processing such cases, such as "senior technical architect interviewer" or "industry-specific recruitment expert." The system performs a matching query in the expert tag library based on the sample characteristics of the trigger signal to determine its most likely category and which expert role should be recommended for handling.

[0067] Step 4806: Based on the suggested expert roles, find the corresponding online expert system or human expert interface, and push case summaries and feature unresolvable alerts.

[0068] Online expert systems refer to rule engines or decision support systems that integrate domain expert knowledge. Human expert interfaces refer to message notification interfaces that connect to real human resource experts. Case summaries contain core information about the matching pair, details of outlier characteristics, analysis of matching failure reasons, and virtual matching rule attempt logs. Feature unresolvable alerts are explicit notifications requesting expert intervention. Based on the determined expert role, the system searches the resource pool for available online expert systems or human experts subscribed to this type of alert, and pushes the case summary and alert to them.

[0069] Step 4807: Receive feature correction instructions and rule definitions from expert systems or human experts, generate expert correction rules and store them in the model-assisted knowledge base, and update the feedback training sample set using the corrected samples.

[0070] Feature correction instructions are specific guidelines from experts on how to adjust anomalous feature values. Rule definitions are feature processing logic summarized by experts for judging similar situations. Expert correction rules are executable rules formed by the system after formally encapsulating the instructions and definitions. After receiving these inputs, the system first applies the feature correction instructions to directly modify the feature values ​​of the current sample, forming an expert-corrected sample, and replaces the corresponding sample in the original feedback training sample set with it. Simultaneously, the rule definition is transformed into executable logic code or conditional statements and stored as a high-priority expert correction rule in the model-aided knowledge base. This rule will be invoked before virtual matching rules in the future.

[0071] Step 4808: Based on the updated feedback training sample set, re-trigger the self-learning modeling process, and store the newly generated model prototype in the model library after associating and annotating it with the expert correction rules.

[0072] The system uses the updated feedback training sample set, which incorporates expert correction samples, to re-execute the self-learning modeling process described in step 457. The newly generated model prototype will be better able to learn the patterns of such difficult cases. The system will associate the expert correction rule identifier it is based on with the metadata of this prototype model, forming a "model-rule" association pair. This association pair will be stored as a complete knowledge unit in the model library for easy tracing and combined application later.

[0073] This also includes a handling method for continuous matching failures, which includes: Step 4809: If, within a preset time window, the number of unresolvable characteristic signals received by the same or similar job positions exceeds a preset threshold, it is determined that the target job requirement profile is abnormal, and a job profile revision suggestion is sent to the job posting author.

[0074] A preset time window, such as one week or one month, is defined. A preset threshold is a quantitative threshold set based on historical data. An anomaly in the target job profile refers to issues such as vagueness, contradictions, outdatedness, or overly idealistic descriptions of the job requirements, making it difficult for the model to find a suitable match. Job profile revision suggestions are generated based on frequently occurring unresolvable features, resulting in a targeted modification suggestion document. Examples include suggestions to break down skill requirements, adjust experience years, and clarify cultural preferences and priorities. The system monitors signal frequency; when the number of times the same job ID or job category triggers a signal exceeds a threshold within the time window, revision suggestions are automatically generated and sent to the recruitment manager or hiring manager for that position via email or system message.

[0075] Step 4810: Receive the updated job data from the job poster based on the revision suggestions, replace the original job full-element requirement data, re-execute the full process matching from Step 1 to Step 4, and store the update record along with subsequent successful matching cases as long-term feedback for model and rule optimization.

[0076] The job poster, referring to the revision suggestions, updates the job description, skill requirements, and other data in the recruitment system. The system acquires this updated data and completely replaces the original job requirements data. Subsequently, the system, using this job as the core, re-triggers the entire process from data collection, feature extraction, model matching to result output. Cases successfully matched due to profile optimization are specially marked and stored in association with the initial profile revision record, unresolvable feature records, and the information of the finally successfully matched personnel. This complete closed-loop record provides valuable long-term feedback data for continuous optimization of the matching model, feature quantification standards, and job requirement description specifications.

[0077] This method, through the collection of multi-source data on the entire career lifecycle of personnel and the full-element requirements data of job positions, constructs a dynamically evolving multi-dimensional feature profile. It introduces attention mechanisms and hierarchical analysis to achieve high-precision quantitative matching, effectively solving the problems of strong subjectivity and single dimension in traditional person-job matching. By establishing a verification and iterative optimization closed loop, it uses actual behavioral data to reverse verify and optimize the matching model, and introduces generative adversarial networks for feature compensation and expert system intervention mechanisms, thereby improving the accuracy and reliability of the matching results.

[0078] like Figure 2 As shown, this embodiment of the invention also provides a personnel-job intelligent matching system based on multi-dimensional feature profiles, used to implement the above-mentioned personnel-job intelligent matching method based on multi-dimensional feature profiles. The system includes: The multi-source data acquisition and feature map construction module is used to collect multi-source data on the entire career life cycle of personnel and full-element requirement data of positions. After denoising and standardizing the two types of data, the BERT model is used to extract the semantic features of unstructured data. Combined with the quantitative features of structured data, a personnel-position association feature map is constructed to extract the temporal evolution trajectory of personnel features and the dynamic change features of position requirements, forming a multi-dimensional feature evolution dataset. The dynamic weight model training module is used to identify the core competency features of personnel and the core requirement features of positions based on the evolutionary dataset using deep learning algorithms, introduce an attention mechanism to construct a dynamic weight model, and combine historical matching cases to train and output dynamic weight coefficients for each feature dimension under different job types. The Adaptation Strength Coupling Analysis Module is used to construct a measurement model through the Analytic Hierarchy Process (AHP), combine dynamic weight coefficients to quantify skill adaptability, cultural adaptability, and development potential fit, integrate job priority and personnel career development aspirations to complete the adaptation strength coupling analysis, and obtain adaptation strength scores and dynamic matching trend data. The intelligent matching and result generation module is used to train the trend data based on the improved gradient boosting tree algorithm to obtain the optimal matching model and deploy it. It realizes dynamic intelligent matching through real-time data interaction, filters combinations with a matching degree higher than a preset threshold as candidate results, and triggers supplementary data collection and secondary matching if the preset number is not reached. It outputs candidate results and analysis of matching contribution in each dimension. The matching result verification and optimization module is used to verify and optimize the preliminary matching results. This includes collecting matched personnel behavior data and job performance data, extracting confirmatory behavioral features, comparing and analyzing them with matching features, and determining the target matching result or triggering a model optimization signal based on the verification results.

[0079] The multi-source data acquisition and feature map construction module specifically includes: The data acquisition and standardization unit is used to collect multi-source data on the entire career life cycle of personnel and full-element requirement data of job positions. Outliers are eliminated through interquartile range detection and Z-score standardization, and invalid characters are filtered using regular expressions. The semantic feature extraction unit is used to input text descriptions into a pre-trained BERT model and generate high-dimensional vector representations to extract semantic features from unstructured data. The feature graph construction unit is used to associate personnel attributes with job requirements through a graph structure. Nodes represent entities such as skills or experience, and edges represent the strength of association, thus constructing a personnel-job association feature graph. The temporal evolution analysis unit is used to analyze the characteristic change patterns in personnel historical data, extract the temporal evolution trajectory of personnel characteristics and the dynamic change characteristics of job requirements; The evolutionary dataset generation unit is used to integrate all processed features to form a standardized multidimensional feature evolutionary dataset.

[0080] The dynamic weight model training module specifically includes: The core feature recognition unit is used to automatically learn the core competency features of personnel and the core requirement features of positions from evolutionary datasets using convolutional neural networks or recurrent neural networks; The attention mechanism unit is used to dynamically evaluate the impact of different features on the matching results and assign higher weights to key features such as scarce skills. The weighted model training unit is used to train a dynamic weighted model based on historical successful matching cases using the random forest algorithm, and employs time decay weighted sampling technology to enable the model to learn recent market preferences first. The weight coefficient output unit is used to output the dynamic weight coefficients of each feature dimension under different job types.

[0081] The adaptation strength coupling analysis module specifically includes: Multidimensional quantitative model building unit, used to decompose complex matching problems into target layer, criterion layer and solution layer through the analytic hierarchy process; The adaptation measurement unit is used to calculate skill fit, cultural fit, and development potential fit. Skill fit is obtained by calculating the cosine similarity between the personnel skill vector and the job requirement vector. The coupling analysis unit is used to integrate job urgency with personnel career goals, and uses a weighted summation model to fuse multi-dimensional scores; The scoring output unit is used to generate normalized fit strength scores and dynamic matching trend data obtained through sliding time window analysis.

[0082] The intelligent matching and result generation module specifically includes: The matching model training unit is used to train the optimal matching model based on the improved gradient boosting tree algorithm, with the fit strength score as the target variable, multi-dimensional features as input features, and cross-validation to select the best hyperparameters. The real-time matching unit is used to achieve efficient similarity retrieval through vector database technology, continuously receiving updated data on new job positions and personnel and triggering instant matching calculations. The result filtering unit is used to filter combinations with a matching degree higher than a preset threshold as candidate results. If the preset number is not reached, supplementary data collection and secondary matching are triggered. The contribution analysis unit is used to decompose the marginal impact of each feature on the final score using Shapley values ​​and output the contribution analysis of each dimension.

[0083] The matching result verification and optimization module specifically includes: The verification data collection unit is used to collect matched personnel behavior data and job performance data, including quantifiable behavioral records such as interview evaluation scores, skills test scores, and probationary period project completion rates. The behavioral feature extraction unit is used to extract confirmatory behavioral features from the actual collected data, including key indicators such as skill conversion efficiency, cultural integration speed, and performance achievement cycle. The comparative analysis unit is used to construct a feature comparison matrix and calculate the deviation and consistency index between confirmatory behavioral features and corresponding matching features. The verification decision unit is used to determine the target matching result or trigger the model optimization signal based on the comparison analysis results and the preset verification threshold. The model optimization unit is used to store mismatched cases as feedback training samples, triggering the model's incremental learning process to achieve self-iteration and continuous optimization.

[0084] This system embodiment automates and optimizes the entire process from data collection and intelligent matching to result verification by constructing multi-dimensional feature profiles of personnel and positions. The system can deeply mine the dynamic characteristics of personnel career trajectories and job requirements, and quantify the matching degree through adaptive weight models and coupling analysis, thereby improving the accuracy and efficiency of personnel-job matching. By introducing a verification closed loop and self-learning mechanism based on actual behavioral data, the system can continuously optimize the model and identify potential biases. This not only reduces the subjectivity and risk of recruitment decisions, but also enhances the system's adaptability to market changes and complex cases, thus providing enterprises with a highly reliable, evolvable, and decision-supporting intelligent solution for human resource allocation.

[0085] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0086] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for intelligent matching of personnel and positions based on multi-dimensional feature profiling, characterized in that, include: Step 1: Collect multi-source data on the entire career life cycle of personnel and full-element requirement data of positions. Perform noise reduction and standardization processing on the multi-source data and position data. Use the BERT model to extract the semantic features of unstructured data. Combine the quantitative features of structured data to construct a personnel-position association feature map. Extract the temporal evolution trajectory of personnel features and the dynamic change features of position requirements to form a multi-dimensional feature evolution dataset. Step 2: Based on the multidimensional feature evolution dataset, use deep learning algorithms to identify the core competency features of personnel and the core requirements features of positions, introduce an attention mechanism to construct a dynamic weight model, and combine historical matching cases to train and output the dynamic weight coefficients of each feature dimension under different job types. Step 3: Construct a measurement model using the analytic hierarchy process (AHP), and combine it with the dynamic weight coefficients to quantify skill fit, cultural fit, and development potential fit. Integrate job priority and employee career development aspirations to complete the fit strength coupling analysis, and obtain fit strength scores and dynamic matching trend data. Step 4: Train the dynamic matching trend data based on the improved gradient boosting tree algorithm to obtain the optimal matching model and deploy it. Real-time data interaction is used to achieve dynamic intelligent matching. Combinations with a matching degree higher than a preset threshold are selected as candidate results. If the number of candidate results does not reach the preset number, supplementary data collection and secondary matching are triggered. The candidate results and matching contribution analysis of each dimension are output.

2. The method as described in claim 1, characterized in that, It also includes steps for verifying and optimizing the initial matching results, including: Step 40: Define the candidate results as expected matching results; Step 41: Based on the expected matching results, collect the matched personnel behavior data and job performance data; Step 42: Based on the personnel behavior data and job performance data, extract confirmatory behavioral features; Step 43: Compare and analyze the confirmatory behavioral features with the matching features on which the expected matching results are based; Step 44: When the matching degree between the verification behavior feature and the matching feature is higher than the preset verification threshold, the expected matching result is determined as the target matching result and output. Step 45: When the matching degree between the confirmatory behavioral feature and the matching feature is lower than the preset verification threshold, output the model optimization signal and define the relevant data pairs as feedback training samples.

3. The method as described in claim 2, characterized in that, When outputting the optimized model signal, the process also includes depth processing and model iteration steps, specifically including: Step 450: Collect the feedback training samples into a preset low-confidence matching queue and accumulate the number of samples; Step 451: When the number of samples reaches the preset reliable number, execute the K-means clustering algorithm to analyze the common deviations of the samples; Step 452: Determine the dimensions of the bias characteristics and the types of bias sources based on the cluster analysis results; Step 453: Execute the corresponding correction strategy on the feedback training samples according to the type of deviation source to generate a corrected matching result; Step 454: Based on the corrected matching result, trigger a new round of short-term behavior tracking and collect verification data. When the verification is successful, output the corrected result and update the optimal matching model. When the verification fails, output the model structure defect signal. Step 455: In response to the model structure defect signal, select a basic model architecture based on the preset model library and train it with the feedback training samples to obtain the model to be verified, and evaluate its performance through cross-validation. Step 456: When cross-validation passes, deploy the model to be validated in the shadow system for low-volume A / B testing. After the test passes, define it as an iterative matching model and add it to the set of running models. Step 457: When all the cross-validation of the preset model architectures fails, the self-learning modeling process is triggered. A self-learning model prototype is generated based on the common patterns of the subclusters in the feedback training samples. After training and validation, it is added to the model library.

4. The method as described in claim 3, characterized in that, It also includes steps for deep feature compensation and expert intervention after the model structure defect signal is triggered, specifically including: Step 4800: Based on the feedback training samples corresponding to the model structure defect signal, query the acceptable range of the corresponding deviation feature dimension in the preset talent feature standard distribution table as the feature compensation threshold. Step 4801: Based on the original feature data in the feedback training samples and the feature compensation threshold, determine the outlier feature samples and the feature items to be compensated; Step 4802: Based on the preset generative adversarial network model, generate simulated feature values ​​that meet the feature compensation threshold and are logically consistent with other features of the sample for the feature items to be compensated, replace the original values ​​to form a corrected feature vector, and input the corrected feature vector into the model to be verified for verification. Step 4803: After all the features to be compensated have been processed and the model has been validated, the rule for generating simulated feature values ​​is defined as a virtual matching rule and stored in the model-aided knowledge base; Step 4804: When there are features to be compensated that cannot be generated logically consistent simulated feature values ​​through the generative adversarial network model, output an unresolvable feature signal.

5. The method as described in claim 4, characterized in that, It also includes a method for expert system intervention in response to said feature unresolvable signals, including: Step 4805: Based on the sample that triggered the unresolvable signal of the feature, query the preset human resources expert tag library to determine the category of difficult cases to which the sample belongs and the suggested handling expert role; Step 4806: Process expert roles according to the recommendations, find the corresponding online expert system or human expert interface, and push case summaries and feature unresolved alerts; Step 4807: Receive feature correction instructions and rule definitions from expert systems or human experts, generate expert correction rules and store them in the model-assisted knowledge base, and update the feedback training sample set using the corrected samples. Step 4808: Based on the updated feedback training sample set, re-trigger the self-learning modeling process, and store the newly generated model prototype in the model library after associating and labeling it with the expert correction rules.

6. The method as described in claim 4 or 5, characterized in that, It also includes handling methods in response to continuous matching failures, including: Step 4809: When the number of unresolved characteristic signals received by the same job or similar job within a preset time window exceeds a preset threshold, it is determined that the target job requirement profile is abnormal, and a job profile revision suggestion is sent to the job publisher. Step 4810: Receive the job posting data updated by the job poster based on the revision suggestions, replace the original job requirements data, re-execute the full process matching from Step 1 to Step 4, and associate and store the update record with subsequent successful matching cases.

7. The method as described in claim 1, characterized in that, The multi-source data on the entire career lifecycle of personnel mentioned in step 1 includes career history, skills certification, competency assessment and career planning data; the full-element job requirement data includes job description, skills requirements, organizational culture adaptation requirements and job workload thresholds. The denoising and standardization process includes eliminating outliers through interquartile range detection and Z-score standardization, and filtering invalid characters using regular expressions. When extracting semantic features from unstructured data using the BERT model, the text description is input into a pre-trained language model to generate a high-dimensional vector representation to capture deep semantic information. The construction of the personnel-job association feature map uses a graph structure to associate personnel attributes with job requirements; nodes represent entities such as skills or experience, and edges represent association strength. In step 2, the deep learning algorithm uses convolutional neural networks or recurrent neural networks to automatically learn salient patterns from the evolutionary dataset. In the historical matching cases, positive samples are selected from matching records retained for more than 3 months after employment, and negative samples... The samples are selected from records of high-matching individuals who were rejected or left short-term jobs; in step 2, when training the output dynamic weight coefficients, time-decay weighted sampling is used to allow the model to prioritize learning recent market preferences; in step 3, the skill fit is obtained by calculating the cosine similarity between the personnel skill vector and the job requirement vector, while also considering hard skill matching and soft skill semantic similarity; the cultural fit is calculated based on the overlap between organizational culture characteristics and individual job preferences; the development potential fit is quantified by analyzing the consistency between the personnel's learning trajectory and the job promotion path; the fit strength coupling analysis in step 3 uses a weighted summation model to fuse multi-dimensional scores, and the fit strength score is a normalized comprehensive value ranging from 0 to 1.

8. The method as described in claim 1, characterized in that, The improved gradient boosting tree algorithm described in step 4 is an optimized version based on the traditional GBDT, incorporating regularization terms and an early stopping mechanism. When training the optimal matching model, the fit strength score is used as the target variable, multidimensional features are used as input features, and cross-validation is employed to select the optimal hyperparameters. The real-time data interaction described in step 4 achieves efficient similarity retrieval through vector database technology, and uses LanceDB's IVF-PQ index to accelerate the query. The matching contribution analysis described in step 4 uses Shapley values ​​to decompose the marginal impact of each feature on the final score.

9. A personnel-job intelligent matching system based on multi-dimensional feature profiling, used to implement the method described in any one of claims 1-8, characterized in that, include: The multi-source data acquisition and feature map construction module is used to collect multi-source data on the entire career life cycle of personnel and full-element requirement data of positions. After denoising and standardizing the two types of data, the BERT model is used to extract the semantic features of unstructured data. Combined with the quantitative features of structured data, a personnel-position association feature map is constructed to extract the temporal evolution trajectory of personnel features and the dynamic change features of position requirements, forming a multi-dimensional feature evolution dataset. The dynamic weight model training module is used to identify the core competency features of personnel and the core requirement features of positions based on the evolutionary dataset using deep learning algorithms, introduce an attention mechanism to construct a dynamic weight model, and combine historical matching cases to train and output dynamic weight coefficients for each feature dimension under different job types. The Adaptation Strength Coupling Analysis Module is used to construct a measurement model through the Analytic Hierarchy Process (AHP), combine the dynamic weight coefficients to quantify skill adaptability, cultural adaptability, and development potential fit, integrate job priority and personnel career development aspirations to complete the adaptation strength coupling analysis, and obtain adaptation strength scores and dynamic matching trend data. The intelligent matching and result generation module is used to train the trend data based on the improved gradient boosting tree algorithm to obtain the optimal matching model and deploy it. It realizes dynamic intelligent matching through real-time data interaction, filters combinations with a matching degree higher than a preset threshold as candidate results, and triggers supplementary data collection and secondary matching if the preset number is not reached. It outputs candidate results and analysis of matching contribution in each dimension. The matching result verification and optimization module is used to verify and optimize the preliminary matching results. This includes collecting matched personnel behavior data and job performance data, extracting confirmatory behavioral features, comparing and analyzing them with matching features, and determining the target matching result or triggering a model optimization signal based on the verification results.

10. The system as described in claim 9, characterized in that, The multi-source data acquisition and feature map construction module specifically includes: The data acquisition and standardization unit is used to collect multi-source data on the entire career life cycle of personnel and full-element requirement data of job positions. Outliers are eliminated through interquartile range detection and Z-score standardization, and invalid characters are filtered using regular expressions. The semantic feature extraction unit is used to input text descriptions into a pre-trained BERT model and generate high-dimensional vector representations to extract semantic features from unstructured data. The feature graph construction unit is used to associate personnel attributes with job requirements through a graph structure. Nodes represent entities such as skills or experience, and edges represent the strength of association, thus constructing a personnel-job association feature graph. The temporal evolution analysis unit is used to analyze the characteristic change patterns in personnel historical data, extract the temporal evolution trajectory of personnel characteristics and the dynamic change characteristics of job requirements; The evolutionary dataset generation unit is used to integrate all processed features to form a standardized multidimensional feature evolutionary dataset; The dynamic weight model training module specifically includes: The core feature recognition unit is used to automatically learn the core competency features of personnel and the core requirement features of positions from evolutionary datasets using convolutional neural networks or recurrent neural networks. The attention mechanism unit is used to dynamically evaluate the impact of different features on the matching results and assign higher weights to key features such as scarce skills. The weighted model training unit is used to train a dynamic weighted model based on historical successful matching cases using the random forest algorithm, and employs time decay weighted sampling technology to enable the model to learn recent market preferences first. The weight coefficient output unit is used to output the dynamic weight coefficients of each feature dimension under different job types.

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