Rapid human resource talent matching system

Abstract

The invention discloses a human resource talent rapid matching system, which comprises a data acquisition module, and is characterized in that the data acquisition module is used for acquiring multi-source data of an enterprise terminal, a job seeker terminal and an external auxiliary terminal; the data preprocessing module is used for carrying out redundancy screening, anomaly recognition and standardization processing on the collected multi-source data; the multi-dimensional feature construction module is used for disassembling the preprocessed data into multi-dimensional sub-dimensions and generating feature vectors of the posts and the job seekers; the method has the advantages that redundant and abnormal data are screened through the data preprocessing module, and matching interference is reduced; meanwhile, a multi-dimensional feature vector and dynamic weight calculation mechanism is constructed, so that a matching result better meets the requirements of industry attributes and post levels of enterprises; and multi-module parallel processing and weighted cosine similarity algorithm optimization are adopted, so that the matching response time is shortened.

Description

Technical Field

[0001] This invention belongs to the field of human resources technology, specifically relating to a rapid matching system for human resources talent. Background Technology

[0002] Human resource talent matching refers to the process of using technology and scientific methods to accurately compare a company's talent needs with the abilities, experience, personality and other characteristics of candidates, so as to achieve the best fit between talent and position.

[0003] Human resources talent matching system is an intelligent tool that uses information technology, data analysis and artificial intelligence to accurately compare and efficiently allocate corporate talent and job requirements. Its core lies in using algorithm models to deeply mine the matching degree between talent skills, experience, ability, personality and other traits and corporate job requirements, so as to achieve the best fit between talent and job.

[0004] In the current digital human resource management landscape, precise matching of talent and positions is a core element in improving recruitment efficiency and reducing labor costs. Existing technologies, such as the AI-based rapid talent matching system disclosed in patent publication number CN120297648A, include a feature acquisition module for acquiring several job-seeking features of the current job seeker; a standardization and splicing module for standardizing these features and then splicing them to generate a job-seeking feature vector for the current job seeker; a matrix pre-construction module for pre-constructing a historical hiring matrix of past hires; and an execution module for executing the hiring matching process for the current job seeker based on the most similar historical candidates. The core idea is to acquire the job seeker's job-seeking features and generate a feature vector, then anchor it to the pre-constructed historical hire matrix for similarity assessment, and finally determine the matching result of the current job seeker based on the actual work performance of the historical candidates. However, the aforementioned existing technologies have the following key drawbacks: the data dimension is singular, relying solely on job seekers' job-seeking characteristics and past employee work ratings, without incorporating key information such as the dynamic requirements of corporate positions and the implicit traits of job seekers, resulting in a one-sided matching dimension; there is a lack of data filtering mechanisms, and redundant and abnormal data are not preprocessed, which can easily cause interference and reduce matching accuracy; the matching weights are fixed, and feature weights are not dynamically adjusted according to different industries and job levels, making it difficult for generalized matching results to meet the personalized needs of enterprises. Summary of the Invention

[0005] The purpose of this invention is to provide a rapid matching system for human resources talents, which solves the problems of low matching accuracy, weak anti-interference ability, and insufficient personalized adaptation in the existing technology.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a rapid matching system for human resources personnel, comprising... The data acquisition module is used to acquire multi-source data from enterprises, job seekers, and external auxiliary terminals. The data preprocessing module is used to perform redundancy filtering, anomaly identification, and standardization processing on the collected multi-source data. A multi-dimensional feature construction module is used to decompose the preprocessed data into multi-dimensional sub-dimensions and generate feature vectors for job positions and job seekers. A dynamic weight calculation module is used to adjust the weights of each feature item in the feature vector according to industry attributes, job level, and industry development trends. A similarity matching module is used to calculate the similarity between the feature vectors of job positions and job seekers based on dynamic weights, and to generate a candidate pool. A matching result verification module is used to perform cross-validation, threshold adjustment, and manual review of the candidate pool results. The results output module is used to generate a structured matching report and push the results to multiple terminals of enterprises and job seekers. The permission management module is used to implement hierarchical role-based access control. A logging module, which is used to record the entire process of system operation and abnormal information; An anomaly warning module is used to identify anomalies in the data matching process, trigger warnings, and track and handle them.

[0007] As a preferred embodiment of the present invention, the data acquisition module performs the following steps to acquire multi-source data: Step 1: Connect to the enterprise recruitment system interface to obtain the basic job requirements, dynamic job requirements and enterprise evaluation criteria. The basic job requirements include education, major and years of work experience. The dynamic job requirements include project cycle, team size and collaboration frequency. The enterprise evaluation criteria include performance weight and ability priority. Step 2: Connect with job seekers' resume filling platforms to obtain their explicit and implicit characteristics. Explicit characteristics include academic certificates, work experience, and skills certificates, while implicit characteristics include career assessment results, feedback from past projects, and learning ability scores. Step 3: Call the industry data service platform interface to obtain external auxiliary data, including industry talent supply and demand index, job salary benchmarks, and skills update cycle data; Step 4: Open the manual supplementation portal to support HR departments of enterprises in supplementing special requirements for positions and job seekers in supplementing key experience information.

[0008] As a preferred embodiment of the present invention, the data preprocessing module performs data filtering and purification steps as follows: Step 1: Activate the redundant data filtering unit, input job keywords, perform keyword matching on the job seeker data, filter out information related to the job, and remove part-time experience and irrelevant skill certificate data that are not related to the job. Step 2: Activate the abnormal data identification unit, call the blockchain evidence storage interface, compare the job seeker's educational information and certificate information with the blockchain evidence storage data, and at the same time call the background check interface of past employers to verify the description of work results, identify and mark false data such as forged academic qualifications and exaggerated work results; Step 3: Activate the data standardization unit, use natural language processing technology to convert unstructured data into structured data of project outcome quantitative indicators, and use data mapping algorithms to uniformly convert scoring data of different formats into standardized scores of 0-100.

[0009] As a preferred embodiment of the present invention, the multi-dimensional feature construction module implements the feature vector construction steps as follows: Step 1: Start the job feature construction unit, break down the preprocessed enterprise data into basic requirements dimension, capability requirement dimension, and dynamic adaptation dimension, set several feature items for each sub-dimension, and quantify and assign values ​​to each feature item; Step 2: Using a vector generation algorithm, the quantified multi-dimensional features of the job are integrated into a 128-dimensional job feature vector, where each dimension corresponds to the quantized value of a feature. Step 3: Activate the job seeker feature construction unit, decompose the preprocessed job seeker data into explicit ability dimension, implicit trait dimension, and career development dimension, set several feature items for each sub-dimension, and quantify and assign values ​​to each feature item; Step 4: Using the same vector generation algorithm as the job feature vector, integrate the quantified multi-dimensional features of job seekers into a 128-dimensional job seeker feature vector to ensure that the dimensions of the job feature vector and the job seeker feature vector are consistent.

[0010] As a preferred embodiment of the present invention, the dynamic weight calculation module implements the weight adjustment steps as follows: Step 1: Activate the industry weight adaptation unit, obtain the industry information of the enterprise, call the external industry database, and extract the feature item weight benchmark value corresponding to the core capabilities of the industry; Step 2: Activate the job level weight adjustment unit, obtain job level information, and adjust the industry weight benchmark value according to the job level; Step 3: Activate the real-time weight update unit to obtain skill update cycle data from external auxiliary data, and identify the feature items that need to be updated in weight every quarter; Step 4: Receive the enterprise's personalized weight adjustment request, and make secondary fine-tuning of the weights of the core feature items based on the capability priority list submitted by the enterprise.

[0011] As a preferred embodiment of the present invention, the similarity matching module performs similarity calculation and candidate pool generation as follows: Step 1: Start the weighted cosine similarity calculation unit to obtain the job feature vector, job seeker feature vector, and dynamic weights of each feature item; Step 2: Calculate the numerator: Multiply the quantified values ​​of the corresponding feature items in the job feature vector and the job seeker feature vector by the dynamic weight of the feature item, and then sum all the multiplication results; Step 3: Calculate the denominator: Calculate the sum of the squares of the product of the quantified value of each feature item in the job feature vector and its corresponding weight, and the sum of the squares of the product of the quantified value of each feature item in the job applicant feature vector and its corresponding weight. Take the square root of each sum and then multiply the two square roots. Step 4: Calculate the similarity score: Divide the numerator by the denominator to obtain the similarity score between the job posting and the job seeker; Step 5: Start the candidate pool generation unit, set an initial threshold, include job seekers with similarity scores greater than the initial threshold into the candidate pool, sort them from high to low scores and label their rankings.

[0012] As a preferred embodiment of the present invention, the matching result verification module performs the following steps for result verification: Step 1: Start the cross-validation unit, call the historical matching data in the data update module, filter the successful matching cases of the same type of job in the past year, and extract the feature vectors of historical successful candidates; Step 2: Calculate the overlap between the feature vector of the current candidate and the feature vector of the historical successful candidates. The overlap is calculated as the proportion of feature terms with an absolute difference of quantization value ≤ 5 among the same feature terms in both candidates to the total number of feature terms. Step 3: Eliminate candidates with an overlap ratio below 0.6, and retain candidates with an overlap ratio ≥ 0.6 to proceed to the next verification step; Step 4: Activate the dynamic threshold adjustment unit to obtain the urgency information of enterprise recruitment. When recruiting urgently, the verification threshold will be reduced from 0.6 to 0.55, while the threshold will remain at 0.6 for regular recruitment and increased to 0.65 for lenient recruitment. Step 5: Activate the manual review interface unit, open the access point for viewing the detailed information of the top 3 job seekers in the candidate pool to the company's HR, receive the review results from HR, and enter the final matching result list for candidates confirmed by HR, and remove candidates who do not pass from the candidate pool.

[0013] As a preferred embodiment of the present invention, the result output module implements the report generation and push steps as follows: Step 1: Activate the matching report generation unit, obtain the final matching result list, and generate a structured report for each candidate. The report includes the job seeker-job matching score, matching details for each dimension, and reasons for recommendation. The specific scores for each dimension of matching details should be marked. Step 2: Add gap analysis content to the job seeker report to compare the differences between job seeker characteristics and job requirements, and identify the skills that need to be improved; Step 3: Activate the multi-terminal push unit to obtain the receiving addresses of the enterprise HR management terminal and the job seeker terminal; Step 4: Push the enterprise report to the HR management terminal, supporting HR to view, download and print online; push the job seeker report to job seekers via SMS link and APP notification, and job seekers can view the report by clicking the link or entering the APP.

[0014] Compared with the prior art, the beneficial effects of the present invention are: The data preprocessing module filters out redundant and abnormal data to reduce matching interference; at the same time, it constructs a multi-dimensional feature vector and a dynamic weight calculation mechanism to make the matching results more in line with the industry attributes and job level requirements of enterprises. Multi-module parallel processing and weighted cosine similarity algorithm optimization shorten the matching response time; The dynamic weight calculation module supports differentiated weight adjustments based on industry and job level, while the anomaly warning module can provide real-time feedback based on the company's recruitment situation, meeting the personalized needs of different companies. The access control module implements hierarchical access control, and the logging module saves the entire process operation log to ensure data security and operation traceability, which complies with data privacy protection regulations. Attached Figure Description

[0015] Figure 1 This is a diagram illustrating the structure of the rapid matching system for human resources talent according to the present invention. Detailed Implementation

[0016] Please see Figure 1 This invention provides a rapid matching system for human resources talent, including... The data acquisition module is used to acquire multi-source data from enterprises, job seekers, and external auxiliary sources. The steps for the data acquisition module to acquire multi-source data are as follows: Step 1: Connect to the enterprise recruitment system interface to obtain the basic job requirements, dynamic job requirements and enterprise evaluation criteria. The basic job requirements include education, major and years of work experience. The dynamic job requirements include project cycle, team size and collaboration frequency. The enterprise evaluation criteria include performance weight and ability priority. Step 2: Connect with job seekers' resume filling platforms to obtain their explicit and implicit characteristics. Explicit characteristics include academic certificates, work experience, and skills certificates, while implicit characteristics include career assessment results, feedback from past projects, and learning ability scores. Step 3: Call the industry data service platform interface to obtain external auxiliary data, including industry talent supply and demand index, job salary benchmarks, and skills update cycle data; Step 4: Open the manual supplementation portal to support HR departments of enterprises in supplementing special requirements for positions and job seekers in supplementing key experience information. The data preprocessing module is used to perform redundancy filtering, anomaly identification, and standardization on the collected multi-source data. The data preprocessing module implements the following steps for data filtering and purification: Step 1: Activate the redundant data filtering unit, input job keywords, perform keyword matching on the job seeker data, filter out information related to the job, and remove part-time experience and irrelevant skill certificate data that are not related to the job. Step 2: Activate the abnormal data identification unit, call the blockchain evidence storage interface, compare the job seeker's educational information and certificate information with the blockchain evidence storage data, and at the same time call the background check interface of past employers to verify the description of work results, identify and mark false data such as forged academic qualifications and exaggerated work results; Step 3: Activate the data standardization unit, use natural language processing technology to convert unstructured data into structured data of project outcome quantitative indicators, and use data mapping algorithms to uniformly convert scoring data of different formats into standardized scores of 0-100.

[0017] The multi-dimensional feature construction module decomposes the preprocessed data into multiple sub-dimensions to generate feature vectors for job positions and job seekers. The steps for feature vector construction by the multi-dimensional feature module are as follows: Step 1: Start the job feature construction unit, break down the preprocessed enterprise data into basic requirements dimension, capability requirement dimension, and dynamic adaptation dimension, set several feature items for each sub-dimension, and quantify and assign values ​​to each feature item; Step 2: Using a vector generation algorithm, the quantified multi-dimensional features of the job are integrated into a 128-dimensional job feature vector, where each dimension corresponds to the quantized value of a feature. Step 3: Activate the job seeker feature construction unit, decompose the preprocessed job seeker data into explicit ability dimension, implicit trait dimension, and career development dimension, set several feature items for each sub-dimension, and quantify and assign values ​​to each feature item; Step 4: Using the same vector generation algorithm as the job feature vector, integrate the quantified multi-dimensional features of job seekers into a 128-dimensional job seeker feature vector to ensure that the dimensions of the job feature vector and the job seeker feature vector are consistent. The dynamic weight calculation module adjusts the weights of each feature item in the feature vector based on industry attributes, job level, and industry development trends. The steps for weight adjustment by the dynamic weight calculation module are as follows: Step 1: Activate the industry weight adaptation unit, obtain the industry information of the enterprise, call the external industry database, and extract the feature item weight benchmark value corresponding to the core capabilities of the industry; Step 2: Activate the job level weight adjustment unit, obtain job level information, and adjust the industry weight benchmark value according to the job level; Step 3: Activate the real-time weight update unit to obtain skill update cycle data from external auxiliary data, and identify the feature items that need to be updated in weight every quarter; Step 4: Receive the enterprise's personalized weight adjustment request, and make secondary fine-tuning of the weights of the core feature items based on the capability priority list submitted by the enterprise.

[0018] The similarity matching module calculates the similarity between the feature vectors of job postings and job seekers based on dynamic weights, and generates a candidate pool. The steps for similarity calculation and candidate pool generation in the similarity matching module are as follows: Step 1: Start the weighted cosine similarity calculation unit to obtain the job feature vector, job seeker feature vector, and dynamic weights of each feature item; Step 2: Calculate the numerator: Multiply the quantified values ​​of the corresponding feature items in the job feature vector and the job seeker feature vector by the dynamic weight of the feature item, and then sum all the multiplication results; Step 3: Calculate the denominator: Calculate the sum of the squares of the product of the quantified value of each feature item in the job feature vector and its corresponding weight, and the sum of the squares of the product of the quantified value of each feature item in the job applicant feature vector and its corresponding weight. Take the square root of each sum and then multiply the two square roots. Step 4: Calculate the similarity score: Divide the numerator by the denominator to obtain the similarity score between the job posting and the job seeker; Step 5: Activate the candidate pool generation unit, set an initial threshold, include job seekers with similarity scores greater than the initial threshold into the candidate pool, sort them from highest to lowest score, and label their ranking. The matching result verification module is used to perform cross-validation, threshold adjustment, and manual review of the candidate pool results. The steps for implementing result verification in the matching result verification module are as follows: Step 1: Start the cross-validation unit, call the historical matching data in the data update module, filter the successful matching cases of the same type of job in the past year, and extract the feature vectors of historical successful candidates; Step 2: Calculate the overlap between the feature vector of the current candidate and the feature vector of the historical successful candidates. The overlap is calculated as the proportion of feature terms with an absolute difference of quantization value ≤ 5 among the same feature terms in both candidates to the total number of feature terms. Step 3: Eliminate candidates with an overlap ratio below 0.6, and retain candidates with an overlap ratio ≥ 0.6 to proceed to the next verification step; Step 4: Activate the dynamic threshold adjustment unit to obtain the urgency information of enterprise recruitment. When recruiting urgently, the verification threshold will be reduced from 0.6 to 0.55, while the threshold will remain at 0.6 for regular recruitment and increased to 0.65 for lenient recruitment. Step 5: Activate the manual review interface unit, open the access point for viewing the detailed information of the top 3 job seekers in the candidate pool to the company's HR, receive the review results from HR, and enter the final matching result list for candidates confirmed by HR, and remove candidates who do not pass from the candidate pool. The results output module generates structured matching reports and pushes the results to multiple platforms for both companies and job seekers. The steps for report generation and delivery in the results output module are as follows: Step 1: Activate the matching report generation unit, obtain the final matching result list, and generate a structured report for each candidate. The report includes the job seeker-job matching score, matching details for each dimension, and reasons for recommendation. The specific scores for each dimension of matching details should be marked. Step 2: Add gap analysis content to the job seeker report to compare the differences between job seeker characteristics and job requirements, and identify the skills that need to be improved; Step 3: Activate the multi-terminal push unit to obtain the receiving addresses of the enterprise HR management terminal and the job seeker terminal; Step 4: Push the enterprise report to the HR management terminal, supporting HR to view, download and print online; push the job seeker report to job seekers via SMS link and APP notification, and job seekers can view the report by clicking the link or entering the APP. The access control module is used to implement hierarchical role-based access control. The steps for implementing hierarchical access control in the access control module are as follows: Step 1: Start the role definition unit, define four fixed roles: "Super Administrator", "Enterprise HR", "Job Seeker" and "System Maintainer", and clarify the core responsibilities of each role; Step 2: Activate the permission allocation unit and assign basic permissions to each role: the super administrator has full system operation permissions, the company HR only has the permission to view the company's job data and manage the matching results, job seekers only have the permission to view their own matching reports, and the system maintainer only has the permission to configure system parameters and view logs; Step 3: Support enterprise HR to apply for personalized permissions, such as cross-departmental job data viewing permissions. After the super administrator approves the application, the permission will be temporarily granted, and the maximum period of the permission period shall not exceed 30 days. Step 4: Activate the permission audit unit to record the operation behavior of each role, including operation time, operation subject (role ID), operation content and operation result, and form permission audit log. The log retention period is consistent with the system operation log.

[0019] The logging module is used to record the entire process of system operations and exception information; The anomaly warning module is used to identify anomalies in the data matching process, trigger warnings, and track and handle them. The steps for the logging module and the exception warning module to work together to achieve log management and exception handling are as follows: Start the operation log unit to capture the operation behavior of each module in the system in real time, and record the module call time, operation subject (role ID), operation content and operation result (success / failure). The error logging unit is activated to automatically record the error code, cause, time of occurrence, and affected modules when the system encounters an anomaly (such as data acquisition failure or similarity calculation error). Start the log storage unit and use a distributed storage architecture to store operation logs and error logs on multiple server nodes to ensure that logs are not lost. The log retention period is set to at least 3 years. It provides log retrieval functionality, supporting log searches by time range, module name, operation type, error code, and other dimensions. The search results can be exported to Excel format. Activate the data anomaly early warning unit and set anomaly thresholds: when the proportion of false data exceeds 5% or the data collection success rate is less than 90%, monitor the processing results of the data preprocessing module in real time, and send SMS and APP alerts to the system maintenance personnel when the thresholds are reached. The alert information includes the anomaly type, the amount of abnormal data, and processing suggestions. Activate the matching anomaly warning unit and set anomaly threshold: the number of candidates in the pool is less than the number of employees required by the company (when the number of employees required is ≥3, the number of candidates in the pool is <3), the matching success rate (final number of employees hired / number of candidates in the pool) is less than 30%, monitor the similarity matching module and subsequent hiring data, and send an alert to the company's HR when the threshold is reached, and recommend adjusting the job requirements or feature weights. The early warning processing tracking unit is activated to record the early warning recipient, the time of receipt, the processing progress and the processing result, generate an early warning processing report, and push it to the super administrator and system maintainer to ensure that the anomaly is resolved in a timely manner.

Claims

1. A rapid matching system for human resources talent, characterized in that: include The data acquisition module is used to acquire multi-source data from enterprises, job seekers, and external auxiliary terminals. The data preprocessing module is used to perform redundancy filtering, anomaly identification, and standardization processing on the collected multi-source data. A multi-dimensional feature construction module is used to decompose the preprocessed data into multi-dimensional sub-dimensions and generate feature vectors for job positions and job seekers. A dynamic weight calculation module is used to adjust the weights of each feature item in the feature vector according to industry attributes, job level, and industry development trends. A similarity matching module is used to calculate the similarity between the feature vectors of job positions and job seekers based on dynamic weights, and to generate a candidate pool. A matching result verification module is used to perform cross-validation, threshold adjustment, and manual review of the candidate pool results. The results output module is used to generate a structured matching report and push the results to multiple terminals of enterprises and job seekers. The permission management module is used to implement hierarchical role-based access control. A logging module, which is used to record the entire process of system operation and abnormal information; An anomaly warning module is used to identify anomalies in the data matching process, trigger warnings, and track and handle them.

2. The rapid matching system for human resources talent according to claim 1, characterized in that: The data acquisition module implements the following steps for acquiring multi-source data: Step 1: Connect to the enterprise recruitment system interface to obtain the basic job requirements, dynamic job requirements and enterprise evaluation criteria. The basic job requirements include education, major and years of work experience. The dynamic job requirements include project cycle, team size and collaboration frequency. The enterprise evaluation criteria include performance weight and ability priority. Step 2: Connect with job seekers' resume filling platforms to obtain their explicit and implicit characteristics. Explicit characteristics include academic certificates, work experience, and skills certificates, while implicit characteristics include career assessment results, feedback from past projects, and learning ability scores. Step 3: Call the industry data service platform interface to obtain external auxiliary data, including industry talent supply and demand index, job salary benchmarks, and skills update cycle data; Step 4: Open the manual supplementation portal to support HR departments of enterprises in supplementing special requirements for positions and job seekers in supplementing key experience information.

3. The rapid matching system for human resources talent according to claim 1, characterized in that: The data preprocessing module performs data filtering and purification steps as follows: Step 1: Activate the redundant data filtering unit, input job keywords, perform keyword matching on the job seeker data, filter out information related to the job, and remove part-time experience and irrelevant skill certificate data that are not related to the job. Step 2: Activate the abnormal data identification unit, call the blockchain evidence storage interface, compare the job seeker's educational information and certificate information with the blockchain evidence storage data, and at the same time call the background check interface of past employers to verify the description of work results, identify and mark false data such as forged academic qualifications and exaggerated work results; Step 3: Activate the data standardization unit, use natural language processing technology to convert unstructured data into structured data of project outcome quantitative indicators, and use data mapping algorithms to uniformly convert scoring data of different formats into standardized scores of 0-100.

4. The rapid matching system for human resources talent according to claim 1, characterized in that: The steps for constructing feature vectors using the multi-dimensional feature construction module are as follows: Step 1: Start the job feature construction unit, break down the preprocessed enterprise data into basic requirements dimension, capability requirement dimension, and dynamic adaptation dimension, set several feature items for each sub-dimension, and quantify and assign values ​​to each feature item; Step 2: Using a vector generation algorithm, the quantified multi-dimensional features of the job are integrated into a 128-dimensional job feature vector, where each dimension corresponds to the quantized value of a feature. Step 3: Activate the job seeker feature construction unit, decompose the preprocessed job seeker data into explicit ability dimension, implicit trait dimension, and career development dimension, set several feature items for each sub-dimension, and quantify and assign values ​​to each feature item; Step 4: Using the same vector generation algorithm as the job feature vector, integrate the quantified multi-dimensional features of job seekers into a 128-dimensional job seeker feature vector to ensure that the dimensions of the job feature vector and the job seeker feature vector are consistent.

5. The rapid matching system for human resources talent according to claim 1, characterized in that: The dynamic weight calculation module implements the weight adjustment steps as follows: Step 1: Activate the industry weight adaptation unit, obtain the industry information of the enterprise, call the external industry database, and extract the feature item weight benchmark value corresponding to the core capabilities of the industry; Step 2: Activate the job level weight adjustment unit, obtain job level information, and adjust the industry weight benchmark value according to the job level; Step 3: Activate the real-time weight update unit to obtain skill update cycle data from external auxiliary data, and identify the feature items that need to be updated in weight every quarter; Step 4: Receive the enterprise's personalized weight adjustment request, and make secondary fine-tuning of the weights of the core feature items based on the capability priority list submitted by the enterprise.

6. The rapid matching system for human resources talent according to claim 1, characterized in that: The similarity matching module performs similarity calculation and candidate pool generation in the following steps: Step 1: Start the weighted cosine similarity calculation unit to obtain the job feature vector, job seeker feature vector, and dynamic weights of each feature item; Step 2: Calculate the numerator: Multiply the quantified values ​​of the corresponding feature items in the job feature vector and the job seeker feature vector by the dynamic weight of the feature item, and then sum all the multiplication results; Step 3: Calculate the denominator: Calculate the sum of the squares of the product of the quantified value of each feature item in the job feature vector and its corresponding weight, and the sum of the squares of the product of the quantified value of each feature item in the job applicant feature vector and its corresponding weight. Take the square root of each sum and then multiply the two square roots. Step 4: Calculate the similarity score: Divide the numerator by the denominator to obtain the similarity score between the job posting and the job seeker; Step 5: Start the candidate pool generation unit, set an initial threshold, include job seekers with similarity scores greater than the initial threshold into the candidate pool, sort them from high to low scores and label their rankings.

7. The rapid matching system for human resources talent according to claim 1, characterized in that: The matching result verification module performs the following steps to verify the results: Step 1: Start the cross-validation unit, call the historical matching data in the data update module, filter the successful matching cases of the same type of job in the past year, and extract the feature vectors of historical successful candidates; Step 2: Calculate the overlap between the feature vector of the current candidate and the feature vector of the historical successful candidates. The overlap is calculated as the proportion of feature terms with an absolute difference of quantization value ≤ 5 among the same feature terms in both candidates to the total number of feature terms. Step 3: Eliminate candidates with an overlap ratio below 0.6, and retain candidates with an overlap ratio ≥ 0.6 to proceed to the next verification step; Step 4: Activate the dynamic threshold adjustment unit to obtain the urgency information of enterprise recruitment. When recruiting urgently, the verification threshold will be reduced from 0.6 to 0.55, while the threshold will remain at 0.6 for regular recruitment and increased to 0.65 for lenient recruitment. Step 5: Activate the manual review interface unit, open the access point for viewing the detailed information of the top 3 job seekers in the candidate pool to the company's HR, receive the review results from HR, and enter the final matching result list for candidates confirmed by HR, and remove candidates who do not pass from the candidate pool.

8. The human resources talent rapid matching system according to claim 1, characterized in that: The steps for the result output module to generate and push reports are as follows: Step 1: Activate the matching report generation unit, obtain the final matching result list, and generate a structured report for each candidate. The report includes the job seeker-job matching score, matching details for each dimension, and reasons for recommendation. The specific scores for each dimension of matching details should be marked. Step 2: Add gap analysis content to the job seeker report to compare the differences between job seeker characteristics and job requirements, and identify the skills that need to be improved; Step 3: Activate the multi-terminal push unit to obtain the receiving addresses of the enterprise HR management terminal and the job seeker terminal; Step 4: Push the enterprise report to the HR management terminal, allowing HR to view, download, and print it online; The job seeker report will be pushed to job seekers via SMS link and APP notification. Job seekers can view the report by clicking the link or entering the APP.

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