Research data modeling method and system based on post demand information driving

Abstract

The application discloses a research data modeling method and system based on post demand information driving, relates to the technical field of research data processing, and comprises the following steps: S1, collecting post demand data in real time, and performing data preprocessing; S2, judging the heterogeneity level of post skill requirements, thereby performing hierarchical early warning; S3, further performing pseudo-commonness detection of post skills, and constructing a dynamic optimization mechanism; S4, analyzing the mismatch degree of each post skill and overall skill, and taking skill mismatch control measures; S5, fusing post demand data, pseudo-commonness detection results, the mismatch degree of each post skill and overall skill, constructing a visual feedback mechanism, and realizing a self-learning and optimization closed loop; the problems that structural distortion of post demand data in research results is caused by template collection and enterprise plagiarism of post demand data are solved.

Description

Technical Field

[0001] This invention relates to the field of survey data processing technology, specifically to a survey data modeling method and system driven by job requirement information. Background Technology

[0002] With industrial restructuring and high-quality economic development, enterprises are increasingly demanding more diverse and specialized job skills. Facing the new trends of digital transformation and intelligent development in industries, job skill structures are constantly evolving. Enterprises urgently need to accurately and efficiently capture changes in employment needs and drive the continuous optimization of job competency standards. Meanwhile, modern information technology provides strong support for the large-scale, multi-source, and multi-dimensional collection, analysis, and application of job survey data. Data modeling systems based on job demand information can integrate multi-source survey data, dynamically depict industry job demand profiles, provide scientific support for enterprise recruitment, talent development, and curriculum optimization, promote precise matching of people and jobs, and contribute to the high-quality development of enterprises and education.

[0003] For example, invention patent CN118861999A discloses a method for establishing and matching an intelligent job matching model based on deep reinforcement learning, belonging to the field of job matching technology based on computer data processing. This invention trains a newly designed job matching model using deep reinforcement learning on a constructed original dataset, ultimately outputting a matching score between job seekers' resumes and company job postings; and then recommends corresponding job matches. The model includes a feature extraction module and a prediction output module. The feature extraction module extracts and fuses features in three dimensions to obtain multi-granularity attention feature representations. The prediction output module uses a deep Q-network to model the job matching problem as a Markov decision process. This invention can comprehensively capture the multi-dimensional features of job seekers and job positions, greatly improving the richness and accuracy of feature representations. By designing a reasonable state space, action space, and reward function, the model can continuously learn and optimize matching strategies through interaction.

[0004] For example, the invention patent with announcement number CN119249252A discloses a method and system for analyzing enterprise survey data in applied education. Specifically, this method involves conducting a survey of a company's job postings to obtain a data sample. Then, it clusters the data sample based on region, occupation, position, job role, action field, and work context. In the further in-depth analysis of the work context, it fully considers the localized characteristics of companies in different regions. Through horizontal and vertical comparisons, it extracts the differences between different positions and occupations under different real work tasks and processes, making the analysis of enterprise survey data more realistic and enhancing the accuracy and structure of the survey content. This further provides sufficient evidence for the talent training goals and processes of applied education.

[0005] However, in the process of implementing the inventive technical solution in the embodiments of this application, it was found that the above-mentioned technology has at least the following technical problems: In job demand data surveys, there is often a superficial homogenization of job positions. Many of these commonalities actually stem from templated descriptions, industry jargon, and copying between companies during the data collection process, and do not represent the true market demand. This leads to pseudo-commonality and structural mismatch in job skills in the survey results. Such data distortion can mislead model learning and affect the scientific nature of data analysis and the targeted nature of talent training.

[0006] Therefore, in order to address the above issues, there is an urgent need for a survey data modeling method and system driven by job requirement information. Summary of the Invention

[0007] Technical problems to be solved To address the shortcomings of existing technologies, this invention provides a survey data modeling method and system based on job requirement information, which solves the problem of structural distortion of job requirement data in survey results due to templated data collection and corporate plagiarism.

[0008] Technical solution To achieve the above objectives, this invention employs the following technical solution: a survey data modeling method driven by job requirement information, comprising the following steps: S1, real-time collection of job requirement data and preprocessing of the data; S2, determination of the heterogeneity level of job skill requirements based on the preprocessed job requirement data, and tiered early warning based on the heterogeneity level determination results; S3, pseudo-commonality detection of job skills based on the heterogeneity level determination results and using the preprocessed job requirement data, and construction of a dynamic optimization mechanism based on the pseudo-commonality detection results; S4, analysis of the mismatch degree between job skills and overall skills based on the preprocessed job requirement data, and implementation of skill mismatch control measures based on the mismatch degree between job skills and overall skills; S5, integration of job requirement data, pseudo-commonality detection results, and mismatch degree between job skills and overall skills to construct a visual feedback mechanism, achieving self-learning and optimization closed loop.

[0009] Furthermore, the specific process of real-time collection and data preprocessing of job requirement data is as follows: Real-time collection of job requirement data, including: job posting time, job title, industry category, job responsibilities, skill items, skill requirement text, compensation, employment type, number of positions, company information, and employee skill text; each piece of job requirement data is tagged with the collection time, company category, industry category, and geographical location; the data format of the job requirement data is standardized, including unifying the core fields of job title, job responsibilities, and skill requirement text; for duplicate, redundant, and invalid job requirement data, intelligent deduplication algorithms and NLP denoising technology are used to remove noise information; the job posting time and collection time are uniformly converted to a standard format; the job requirement data is also normalized; a job requirement database is constructed to store the job requirement data; and a professional skills ontology and thesaurus are constructed using the job requirement data, performing semantic normalization on synonymous skill requirement text, employee skill text, and job responsibilities, and standardizing and tagging job elements.

[0010] Furthermore, based on the preprocessed job requirement data, the specific process for determining the heterogeneity level of job skill requirements is as follows: For the same job, count the total number of companies participating in the survey; obtain the skill items for this job from a specific company and count the total number of skill items, and analyze the skill items for this job from each company, comparing them with other companies, and count the number of skills that appear only in this company and not in any other company, recorded as the number of unique skill items of the i-th company; obtain the skill requirement text for the job, process the skill requirement text using the BERT model, and transform each skill requirement into a fixed-length numerical vector, thereby obtaining the skill requirement data. For each skill requirement vector, the Euclidean distance is used to calculate the pairwise distance between all skill requirement vectors. The average of these distances is then calculated to obtain the average text distance for the same job skill requirement in the i-th company. This average text distance is then multiplied by a distance adjustment weighting factor, and the number of unique skill items in the i-th company is added to obtain the composite value of job skill heterogeneity. This composite value is then divided by the total number of skill items to obtain the job skill heterogeneity ratio. Based on the total number of companies, the job skill heterogeneity ratio for all companies is calculated, and the average is used to obtain the job skill structure dispersion value.

[0011] Furthermore, based on the heterogeneity level discrimination results, the specific process for graded early warning is as follows: For each type of job, calculate the job skill structure dispersion value, and compare the skill structure dispersion value with the skill dispersion thresholds T1 and T2; when the job skill structure dispersion value is less than or equal to T1, it is identified as having a false commonality risk, triggering the subsequent false commonality detection process: review the confidence level of the job skill requirements for true commonality and adjust the survey data template; optimize the survey questionnaire and data collection rules, and filter out templated and invalid job descriptions; when the job skill structure dispersion value is greater than T1 and less than or equal to T2, only continuous monitoring and dynamic monitoring are maintained; when the job skill structure dispersion value is greater than T2, it is identified as a description disorder risk, generating a description disorder warning prompt, generating optimization suggestions and standardized job description templates based on historical job demand data, recommending description specifications and standard fields, pushing them to the data collection entry in real time, and performing adaptive adjustment of the template at the data collection front end.

[0012] Furthermore, based on the heterogeneity level discrimination results, the specific process of detecting pseudo-commonalities in job skills using preprocessed job requirement data is as follows: 1. Receive jobs with pseudo-commonalities and descriptive disorder risks; 2. Obtain job requirement data from all companies containing jobs with pseudo-commonalities and descriptive disorder risks; 3. Obtain a set of companies, industries, and posting times; 4. Retrieve skill requirement texts; 5. Count companies containing a specific skill 's' to obtain the set of companies where skill 's' appears; 6. Obtain the industry categories of all jobs with pseudo-commonalities and descriptive disorder risks; 7. Retrieve and count industries involving skill 's' to obtain the set of industries where skill 's' appears; 8. Obtain job posting times; 9. Count all time periods in which skill 's' appears to obtain the set of posting times for skill 's'; 10. Simultaneously, count the... The number of unique companies containing skill 's' in the skill requirement text is determined as follows: Identify each job requirement data point that belongs to the company set, industry set, and posting time set, and contains skill 's'. Sum the number of job requirement data points to obtain the three-dimensional combination number of skill 's' occurrences. Add 1 to the number of unique companies for skill 's' and take the natural logarithm to obtain the company distribution breadth adjustment factor. Simultaneously calculate the total number of combinations at the intersection of the entire company set, industry set, and posting time set during the survey period. Multiply the three-dimensional combination number of skill 's' occurrences by the company distribution breadth adjustment factor to obtain the skill cross-dimensional distribution breadth value. Divide the skill cross-dimensional distribution breadth value by the total number of combinations to obtain the true commonality confidence value of job skill item 's'.

[0013] Furthermore, the specific process of constructing a dynamic optimization mechanism based on the pseudo-commonality detection results is as follows: Calculate the true commonality confidence value for each skill requirement, and compare the true commonality confidence value with the confidence threshold; when the true commonality confidence value is less than or equal to the confidence threshold, it is considered a pseudo-commonality and templated pseudo-relevance, and this skill description is immediately blocked from subsequent job requirement data collection, and the survey questionnaire and job description template are adjusted in real time to optimize the skill collection fields and dynamically filter and block invalid skill fields; when the true commonality confidence value is greater than the confidence threshold, it is considered a true commonality, and the skill items with true commonality are included in the key skill list, driving the automatic updating of job templates, intelligent completion of job skill requirement text fields, and automatic recommendation and intelligent adaptation of job description templates, recruitment standards, and personnel development paths.

[0014] Furthermore, combining the preprocessed job requirement data, the specific process for analyzing the degree of mismatch between job skills and overall skills is as follows: The number of jobs involved in the survey is counted, and the skill requirement texts and practitioner skill texts for each job are obtained, resulting in job skill requirement sets and practitioner skill sets, respectively; the number of skills present in the job skill requirement set but not in the practitioner skill set is counted to obtain the number of missing skills; the number of skills present in the practitioner skill set but not in the job skill requirement set is counted to obtain the number of skill redundancies; the union of the job skill requirement set and the practitioner skill set is taken to obtain the total number of skills; the skill redundancy number is multiplied by the redundancy weight factor, and then added to the number of missing skills to obtain the job skill mismatch number; the job skill mismatch number is divided by the total number of skills to obtain the job skill mismatch degree value; based on the number of jobs, the job skill mismatch degree values ​​for all jobs are summed and averaged to obtain the overall skill mismatch degree value.

[0015] Furthermore, based on the degree of mismatch between skills in each position and the overall skill set, the specific process for implementing skill mismatch control measures is as follows: Real-time monitoring of the skill mismatch degree for each position and the overall skill mismatch degree; once the skill mismatch degree exceeds the mismatch threshold, a skill mismatch warning is generated and marked on the visualization panel. Simultaneously, the job description template is linked to adjust and optimize the skill requirement text fields, dynamically filter redundant skill items, and accurately correct the job description; skills with high mismatch incidence are identified and compiled into a list, and adaptive recommendations and continuous optimizations are made to the survey questionnaire, job standards, and job description templates; mismatch types are subdivided and statistically analyzed, all mismatch degree values ​​are archived, and a mismatch case library is constructed, periodically generating trend analysis charts and reports; and job requirement data is continuously collected to fine-tune the skill mismatch degree parameter.

[0016] Furthermore, by integrating job requirement data, pseudo-commonality detection results, and the degree of mismatch between skills in each job and overall skills, a visual feedback mechanism is constructed to achieve a self-learning and optimization closed loop. The specific process is as follows: A multi-dimensional visual display of the distribution of true and pseudo-commonality in jobs and skill mismatch is provided, allowing users to flexibly filter and drill down by industry, region, company, and job type; an interactive feedback mechanism is constructed to support online annotation, correction, and feedback from manual reviewers of pseudo-commonality and mismatch cases, and to collect manually corrected data for parameter tuning; and a report on job commonality, skill mismatch, and industry trend analysis is generated, with regular self-learning iterations and updates to the mismatch case library and parameters to achieve a closed-loop update of intelligent analysis and optimization.

[0017] The second aspect of this invention provides a survey data modeling system driven by job requirement information, comprising: a multi-source job data acquisition and preprocessing module for real-time acquisition of job requirement data and preprocessing the data; a job requirement content feature modeling module for determining the heterogeneity level of job skill requirements based on the preprocessed job requirement data and providing graded early warnings based on the heterogeneity level determination results; a pseudo-commonality detection and real commonality extraction module for detecting pseudo-commonality of job skills based on the heterogeneity level determination results and using the preprocessed job requirement data, and constructing a dynamic optimization mechanism based on the pseudo-commonality detection results; a job skill mismatch structural analysis module for analyzing the degree of mismatch between individual job skills and overall skills in conjunction with the preprocessed job requirement data, and taking skill mismatch control measures based on the degree of mismatch between individual job skills and overall skills; and an intelligent visualization and feedback module for integrating job requirement data, pseudo-commonality detection results, and the degree of mismatch between individual job skills and overall skills to construct a visualization feedback mechanism and achieve a self-learning and optimization closed loop.

[0018] Beneficial effects The present invention has the following beneficial effects: (1) This invention completes the standardization, normalization and noise filtering of job demand data through real-time collection and preprocessing, ensuring that downstream analysis and modeling are based on real, high-quality and structured multidimensional data, effectively supporting the dynamic evolution analysis of demand in complex job scenarios.

[0019] (2) This invention utilizes the algorithm of the dispersion degree and common confidence of job skill structure to distinguish between real commonalities and templated pseudo-commonalities. It automatically adjusts the survey questionnaire and job template for pseudo-commonal items to avoid interference from low-quality and invalid information, thereby improving the scientificity and accuracy of industry surveys and standard setting.

[0020] (3) This invention, through mismatch rate calculation, monitors the structural deviation between job positions and practitioners' skills in real time. For jobs and skills with high mismatch rates, it optimizes skill fields and recommended content by linking job templates and data entry points, continuously reducing job skill mismatch rate and enhancing the job-person matching effect.

[0021] (4) This invention integrates data-driven visualization analysis, periodic reports and interactive feedback to achieve closed-loop self-learning of common job skills, mismatch trends and optimization parameters, continuously optimize the knowledge base and parameters, and help enterprises, industries and education to achieve intelligent upgrading of job competency standards and refined talent training.

[0022] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0023] Figure 1 A flowchart illustrating a survey data modeling method driven by job requirement information; Figure 2 A structural diagram of a survey data modeling system driven by job requirement information; Figure 3 A distribution chart of skill mismatch values ​​for each job position; Figure 4 This is a flowchart of a survey data modeling system driven by job requirement information. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. As those skilled in the art will understand, the described embodiments are only some embodiments of the present invention, and not all embodiments. 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.

[0025] Please see Figures 1-4 This invention provides a technical solution: a survey data modeling method and system driven by job requirement information, such as... Figure 1As shown, the process includes the following steps: S1, real-time collection of job requirement data and preprocessing of the data; S2, determination of the heterogeneity level of job skill requirements based on the preprocessed job requirement data, and tiered early warning based on the heterogeneity level determination results; S3, pseudo-commonality detection of job skills based on the heterogeneity level determination results and using the preprocessed job requirement data, and construction of a dynamic optimization mechanism based on the pseudo-commonality detection results; S4, analysis of the mismatch degree between job skills and overall skills based on the preprocessed job requirement data, and implementation of skill mismatch control measures based on the mismatch degree; S5, integration of job requirement data, pseudo-commonality detection results, and mismatch degree between job skills and overall skills to construct a visual feedback mechanism, achieving self-learning and optimization closed loop.

[0026] Specifically, the real-time collection and preprocessing of job demand data involves: crawling recruitment platforms, company websites, and industry associations, and combining API interfaces, web crawlers, and proactive uploads from companies to collect real-time job demand data. This data includes: job posting time, job title, industry category, job responsibilities, skill items, skill requirements, compensation, employment type, number of positions, company information, and employee skill descriptions. A unique identifier is assigned to each data source to ensure the accuracy of subsequent data tracing and archiving. Each job demand data entry is also tagged with the collection time, company category, industry category, and geographical location. The data format is then standardized. Standardization includes unifying core fields such as job titles, job descriptions, and skill requirements. It also utilizes standard industry dictionaries and job specifications for field matching and mapping to improve data compatibility. For duplicate, redundant, and invalid job requirement data, intelligent deduplication algorithms and NLP denoising techniques are employed to remove noise. Job posting and collection times are uniformly converted to a standard format. Job requirement data is also normalized. A job requirement database is built to store the data. Furthermore, a professional skills ontology and a thesaurus are constructed using the job requirement data. Semantic normalization is performed on synonymous skill requirement texts, practitioner skill texts, and job descriptions, resulting in standardized mapping and tagging of job elements.

[0027] This implementation plan achieves high-quality data traceability and refined archiving by collecting job requirement data in real time through multiple channels and automated methods, combined with unique data identifiers and multi-dimensional tags. Data preprocessing significantly improves the accuracy, compatibility, and usability of job requirement data. Simultaneously, based on standardized data storage and the construction of a professional skills ontology, it achieves unified tagging of synonymous skills and job responsibilities, providing a solid and reliable data foundation for subsequent intelligent analysis and industry standard development, and significantly enhancing the data processing capabilities and analytical value of job requirement surveys.

[0028] Specifically, based on the preprocessed job requirement data, the process for determining the heterogeneity level of job skill requirements is as follows: Cluster the job requirement data by job name; for the same job, count the total number of companies participating in the survey; obtain the skill items for this job from a specific company and count the total number of skill items; analyze the skill items for this job from each company and compare them with other companies, recording the number of skills that appear only in this company and not in any other company as the number of unique skill items for the i-th company; obtain the job skill requirement text and process the skill requirement text using the BERT model. Different text models such as BERT, ERNIE, and RoBERTa can be used depending on the specific industry data. Diversified requirements are achieved by transforming each skill requirement into a fixed-length numerical vector, resulting in a skill requirement vector. For each skill requirement vector, the Euclidean distance is calculated to determine the pairwise distances between all skill requirement vectors. These distances are then summed and averaged to obtain the average text distance for the same job skill requirements in the i-th company. This average text distance is multiplied by a distance adjustment weighting factor, and then added to the number of unique skill items in the i-th company to obtain the composite value of job skill heterogeneity. This composite value is divided by the total number of skill items to obtain the job skill heterogeneity ratio. Based on the total number of companies, the job skill heterogeneity ratio for all companies is calculated, and the average is taken to obtain the job skill structure dispersion value.

[0029] The specific formula for the value of the dispersion of job skill structure is as follows: ; In the formula, The value represents the degree of dispersion of job skill structure, which measures the dispersion of skill requirements for a certain type of job in different companies, i.e., the heterogeneity of job skill requirements. The higher the dispersion value, the greater the difference in skill requirements for the same job among different companies, and the stronger the trend of personalization and diversification. The lower the dispersion value, the more likely there will be template-based and high commonality of skills. This represents the total number of enterprises, reflecting the sample size; This represents the number of unique skill items for the i-th company, measuring the uniqueness of the company's skill requirements; It represents the average text distance of the skill requirements for the same position in the i-th company, measures the diversity of skill descriptions for the same position within the company, and prevents plagiarism by using different words; This represents the total number of skill items for this position in the i-th company, a standardized denominator that reflects the overall scale of skill demand; This represents the distance adjustment weighting factor, which balances the contribution of text distance to dispersion and prevents extreme values ​​from affecting the results. The average text distance of all companies regarding the skill requirements for this position is obtained to form the average text distance dataset. The standard deviation of the average text distance dataset is calculated, and the reciprocal of the standard deviation is used to obtain the distance adjustment weighting factor, which has a value range between 2 and 100.

[0030] This implementation plan effectively extracts the structural differences between unique and common skills across companies by clustering job titles and conducting in-depth comparisons of skill items from multiple enterprises. Furthermore, by combining various text vector models and Euclidean distance algorithms, it accurately quantifies the semantic differences in job skill descriptions. Based on a comprehensive assessment of skill item diversity and text similarity, this approach not only enhances the scientific rigor and automation of job skill heterogeneity analysis but also provides a more objective, refined, and dynamic basis for industry job standardization, enterprise job description optimization, and data-driven talent profiling.

[0031] Specifically, the process of hierarchical early warning based on the heterogeneity level discrimination results is as follows: For each type of job, the skill structure dispersion value is calculated, and the skill structure dispersion value is compared with the skill dispersion thresholds T1 and T2. All thresholds support adaptive updates to achieve tiered adaptation across different periods, regions, and companies. When the job skill structure dispersion value is less than or equal to T1, it is identified as having a false commonality risk, triggering the subsequent false commonality detection process: verifying the confidence level of the job skill requirements for true commonality and adjusting the survey data template; optimizing the survey questionnaire and data collection rules, filtering out templated and invalid job descriptions; and archiving all identified risks. For job requirement data representing pseudo-common risks, it facilitates subsequent source tracing analysis and closed-loop optimization; when the job skill structure dispersion value is greater than T1 and less than or equal to T2, it only maintains continuous monitoring and dynamic monitoring, while recording all job requirement data changes, supporting periodic trend analysis, fluctuation diagnosis and intelligent prediction, ensuring that the early warning chain is not broken; when the job skill structure dispersion value is greater than T2, it is identified as a risk of disordered description, generating a warning prompt for disordered description, generating optimization suggestions and standardized job description templates based on historical job requirement data, recommending description specifications and standard fields, pushing them to the data collection entry in real time, and adaptively adjusting the templates on the data collection front end.

[0032] This implementation plan achieves accurate identification and dynamic response to abnormal skill dispersion across different positions, regions, and time periods through tiered early warning and adaptive threshold adjustment. It not only automatically distinguishes and archives pseudo-common risks and risks with disordered descriptions, but also continuously optimizes survey questionnaires and job description templates by combining data trend analysis and intelligent prediction. This effectively improves the authenticity, standardization, and usability of job demand data, significantly enhancing the scientific and intelligent level of industry job standardization and data-driven decision-making.

[0033] Specifically, based on the heterogeneity level discrimination results, the process of detecting pseudo-commonalities in job skills using preprocessed job requirement data is as follows: 1. Receive jobs with pseudo-commonalities and risks of description disorder; 2. Obtain job requirement data from all companies containing jobs with pseudo-commonalities and risks of description disorder; 3. Statistically obtain company sets, industry sets, and posting time sets. During the statistical analysis of these sets, duplicate and invalid samples are cleaned to ensure accuracy. 4. Retrieve skill requirement texts and count companies containing a specific skill 's' to obtain the company set where skill 's' appears. 5. Obtain the industry categories for all jobs with pseudo-commonalities and risks of description disorder; 6. Retrieve and count industries involving skill 's' to obtain the industry set where skill 's' appears. 7. Obtain job posting times and count all time periods in which skill 's' appears to obtain the posting time set where skill 's' appears. 8. Simultaneously, count the number of unique companies containing skill 's' in all skill requirement texts. The process involves: establishing a multidimensional distribution index for skill s to facilitate efficient subsequent querying and tracing; identifying each job requirement data point that belongs to the enterprise set, industry set, and posting time set, and contains skill s, and summing the number of job requirement data points to obtain the three-dimensional combination number of skill s occurrences; taking the natural logarithm of the number of unique enterprises for skill s to obtain the enterprise distribution breadth adjustment factor; normalizing the order of magnitude of each dimension to ensure the consistency of horizontal comparison of the distribution breadth adjustment factor and improve the algorithm's applicability across industries, regions, and cycles; calculating the total number of combinations at the intersection of the three-dimensional intersection of the entire enterprise set, industry set, and posting time set during the survey period; multiplying the three-dimensional combination number of skill s occurrences by the enterprise distribution breadth adjustment factor to obtain the skill cross-dimensional distribution breadth value; and dividing the skill cross-dimensional distribution breadth value by the total number of combinations to obtain the true commonality confidence value of job skill item s.

[0034] The specific formula for the true commonality confidence value is as follows: ; In the formula, The confidence score represents the true commonality of job skill item s. It is used to measure whether a certain skill s is a real common need in the industry, rather than a template or a local phenomenon. The higher the value, the more repeatedly actual the skill is needed in different companies, industries, time periods, and regions, and the stronger its representativeness and authority. The number of three-dimensional combinations representing the occurrence of skill 's' indicates how many different combinations of enterprises, industries, and time periods have explicitly demanded skill 's'. It measures the true breadth and activity of skill 's distribution across the intersection of these three dimensions: enterprise, industry, and time. This represents the set of companies where skill item s appears. This represents the set of industries in which skill item s appears. This represents the set of release times in which skill item s appears; This represents the number of unique firms with skill s, emphasizing the breadth of firm distribution and preventing localized high-frequency occurrences. The enterprise distribution breadth adjustment factor is used to measure the distribution range of a skill in different enterprises. It is used to adjust the commonality confidence score by weighting the commonality, strengthening the broad commonality, suppressing local and personalized needs, thereby improving the scientificity and robustness of false commonality detection. This represents the total number of combinations, achieving industry-wide benchmark normalization, and allowing for horizontal comparisons across different skills. Represents a set of firms. Indicates a collection of industries. Represents a set of publication times; This indicates the breadth of skill distribution across dimensions. Only skills that appear repeatedly and are widely distributed across multiple enterprises, industries, and time periods will have a high breadth of skill distribution across dimensions. This can effectively filter out templated and locally popular items.

[0035] This implementation plan achieves precise quantification of the distribution characteristics of job skills across multiple dimensions, including enterprise, industry, and time, through multi-dimensional intersection statistics and rigorous data cleaning. By constructing an enterprise distribution breadth adjustment factor and performing normalization processing, the plan effectively improves the horizontal comparability of commonality detection and its adaptability to cross-industry, cross-regional, and cross-period samples. Combined with the calculation of commonality confidence based on three-dimensional combination number and distribution breadth weighting, the plan significantly improves the accuracy of identifying genuine commonalities and significantly suppresses interference from templated and plagiarized pseudo-commonalities.

[0036] Specifically, the process of constructing a dynamic optimization mechanism based on the results of pseudo-commonality detection is as follows: Calculate the true commonality confidence value for each skill requirement, compare the true commonality confidence value with the confidence threshold (which can be adaptively adjusted according to job category and industry type); when the true commonality confidence value is less than or equal to the confidence threshold, it is considered a pseudo-commonality and a templated pseudo-relevance, and this skill description is immediately hidden from subsequent job requirement data collection. The survey questionnaire and job description template are adjusted in real time, skill collection fields are optimized, invalid skill fields are dynamically filtered and hidden, and all hidden and adjusted skill fields and their data change trajectories are recorded; when the true commonality confidence value is greater than the confidence threshold, it is considered a true commonality, and the skill item with true commonality is included in the key skill list, driving automatic updates to job templates, intelligent completion of job skill requirement text fields, and automatic recommendation and intelligent adaptation of job description templates, recruitment standards, and personnel development paths, generating a matching list between the enterprise and talent sides.

[0037] This implementation plan achieves accurate identification and real-time response to commonalities in job skills and templated pseudo-commonalities through pseudo-commonality detection and dynamic threshold adaptation. By filtering out low-confidence and invalid skill fields, it dynamically adjusts survey questionnaires and job description templates and fully records all data change trajectories, ensuring the traceability and efficient iteration of data governance. Simultaneously, intelligent list push and template completion based on genuine common skills optimize recruitment standards and career development paths, achieving precise matching and efficient connection between job requirements and talent supply.

[0038] Specifically, the process of analyzing the mismatch between job skills and overall skills, based on preprocessed job requirement data, is as follows: First, the collected job information and practitioner information are bidirectionally matched and uniquely identified; the number of jobs involved in the survey is counted; and the skill requirement texts for each job and the practitioner skill texts are obtained, resulting in job skill requirement sets and practitioner skill sets, respectively. The data is then grouped and processed in batches according to multiple dimensions such as enterprise, region, industry, and job category, ensuring compatibility with survey projects of different scales and types. The number of skills present in the job skill requirement set but not in the practitioner skill set is counted to obtain the number of skill gaps. The number of skills present in the practitioner's skill set but not in the job skill requirement set is used to obtain the skill redundancy number. The union of the job skill requirement set and the practitioner's skill set is taken to obtain the total number of skills. The skill redundancy number is multiplied by the redundancy weight factor and added to the number of missing skills to obtain the job skill mismatch number. The job skill mismatch number is divided by the total number of skills to obtain the job skill mismatch degree value. Based on the number of jobs, the job skill mismatch degree values ​​of all jobs are accumulated and averaged to obtain the overall skill mismatch degree value. The entire calculation of the mismatch degree value is archived and compared with the industry average to monitor structural changes in jobs and the talent market.

[0039] The specific formula for the overall skill mismatch value is as follows: ; In the formula, The overall skill mismatch value is used to quantify the degree of structural mismatch between job skill requirements and the actual skills of practitioners. The higher the value, the more prominent the overall skill mismatch problem is, indicating an industry risk of a disconnect between job requirements and talent development and difficulty in recruitment. The lower the value, the better the match between job skills and employee skills, and the higher the talent supply and demand fit. This indicates the number of positions, used for batch calculations across all positions. This represents the set of job skill requirements for the j-th position, reflecting the formal skill requirements for the position; This represents the set of skills of practitioners in the j-th position, reflecting the current skill status of practitioners; This indicates the number of skills gaps, which reflects the skill gaps in job requirements that are not yet possessed by current employees; that is, the abilities that companies need but employees do not yet have. This indicates the skill redundancy number, which reflects the skill redundancy that employees actually possess but that is not explicitly required by the job; that is, the abilities that the talent has but the job does not require. The redundancy weight factor is used to balance the impact of skill redundancy on the mismatch degree value and prevent the extreme value on one side from amplifying the result. It is calculated by taking the average of the number of skill deficiencies and the average of the number of skill redundancies for all positions. The redundancy weight factor is obtained by dividing the average of the number of skill deficiencies for all positions by the average of the number of skill redundancies through the average ratio normalization method. The value range is between 0.2 and 2.0.

[0040] Five typical job positions are listed: data analyst, backend developer, product manager, test engineer, and operations engineer. A redundancy weighting factor of 0.5 is set. Based on the different skill requirements of each position and the varying skills possessed by different practitioners, a comprehensive analysis is conducted to calculate the skill mismatch degree for each position. Table 1 shows the skill mismatch degree data for each position.

[0041] Table 1. Data on the degree of skill mismatch for each job position.

[0042] like Figure 3 The figure shows the distribution of skill mismatch values ​​for various positions provided in this application embodiment. The horizontal axis represents five typical positions, and the vertical axis represents the skill mismatch value for each position. The dashed line in the figure represents the overall average mismatch degree, i.e., the mismatch threshold, which is 0.28. According to Table 1 and Figure 3 It can be seen that the skill mismatch degree of data analysts, product managers and test engineers is higher than the mismatch threshold, indicating that there is a more prominent skill mismatch problem in the actual talent allocation of these positions; the mismatch degree of backend development and operation and maintenance engineers is lower than the mismatch threshold, indicating that the talent skills of these two positions are well matched with the job requirements, and the mismatch phenomenon is relatively mild.

[0043] This implementation plan quantifies the lack and redundancy of job skills by accurately matching and grouping job information with practitioners' skills, and realizes the calculation and archiving of the degree of job skill mismatch. At the same time, it can continuously monitor the structural changes in job positions and the talent market, promptly identify skill supply and demand mismatches and matching gaps, and provide a scientific and data-driven decision-making basis for job setting optimization, recruitment strategy adjustment and skills training direction.

[0044] Specifically, the process of implementing skill mismatch control measures based on the degree of mismatch between skills in each position and overall skills is as follows: Establish a task queue for monitoring job mismatches to ensure comprehensive parallel monitoring across multiple positions and batches; monitor the skill mismatch degree for each position and the overall skill mismatch degree in real time; once the skill mismatch degree exceeds the mismatch threshold, generate a skill mismatch warning, mark it on the visualization panel, and notify the relevant position manager to achieve immediate information linkage and risk response; simultaneously, link the job description template, adjust and optimize the skill requirement text fields for the position, dynamically filter redundant skill items, and accurately correct the job description; identify… To avoid frequent skill mismatches, a list is compiled. This list is then used to adaptively recommend and continuously optimize survey questionnaires, job standards, and job description templates, generating a tag cloud of frequently mismatched skills and optimization suggestions. It also supports dynamic configuration of personalized job templates. Mismatch types are further subdivided and statistically analyzed, with all mismatch severity values ​​archived and a mismatch case library built. Trend analysis charts and reports are generated periodically. Job requirement data is continuously collected, and skill mismatch severity parameter parameters are fine-tuned. Combining historical monitoring results with newly collected job requirement data, the skill mismatch severity assessment algorithm and early warning thresholds are continuously optimized to achieve dynamic adaptation and closed-loop optimization of job skill structures.

[0045] This implementation plan enables real-time early warning, intelligent control, and full-process archiving of job skill mismatch risks; it precisely links job description templates and survey questionnaire content to dynamically optimize skill requirement fields and standard configurations. Through a mismatch case library, trend analysis, and reports, it provides scientific, data-driven, and continuous decision support for enterprise human resource management and job setting. Through continuous self-learning and self-adjustment, it achieves dynamic adaptation and optimization of job skill structures, significantly improving the efficient matching between jobs and talent.

[0046] Specifically, by integrating job requirement data, pseudo-commonality detection results, and the degree of mismatch between skills in each job and overall skills, a visual feedback mechanism is constructed to achieve a self-learning and optimization closed loop. The specific process is as follows: A multi-dimensional visual display of the distribution of true and pseudo-commonality in jobs and skill mismatch is provided, allowing users to flexibly filter and drill down by industry, region, company, and job type; an interactive feedback mechanism is built to support online annotation, correction, and feedback from manual reviewers of pseudo-commonality and mismatch cases, and to collect manually corrected data for parameter tuning; all manual feedback and correction processes are recorded in detail, automatically tracing the path of each parameter adjustment and optimization to ensure traceability and closed-loop management; job commonality, skill mismatch, and industry trend analysis reports are generated, and the mismatch case library and parameters are regularly updated through self-learning iteration. Analysis reports and visualization results are automatically pushed to relevant responsible persons, and the analysis parameters, recommendation rules, and display logic are continuously optimized through the self-learning mechanism to achieve a closed-loop update of intelligent analysis and optimization.

[0047] This implementation plan achieves transparent monitoring and intelligent feedback throughout the entire process of job requirements, commonality detection, and skills mismatch analysis through multi-dimensional visualization and interactive feedback mechanisms. It flexibly supports multi-dimensional filtering, in-depth drilling, and manual intervention, automatically archiving every annotation and optimization adjustment to ensure the traceability and closed-loop nature of parameter tuning and result optimization. Combined with regularly generated analysis reports and an automatic push mechanism, it enables continuous self-learning, significantly improving the intelligence, precision, and decision support capabilities of job requirements analysis.

[0048] Reference Figure 2 As shown, the second aspect of this invention provides a survey data modeling system driven by job requirement information, applied to the aforementioned survey data modeling method driven by job requirement information. The system includes: a multi-source job data acquisition and preprocessing module for real-time acquisition of job requirement data and preprocessing the data; a job requirement content feature modeling module for determining the heterogeneity level of job skill requirements based on the preprocessed job requirement data and providing tiered early warnings based on the heterogeneity level determination results; a pseudo-commonality detection and real commonality extraction module for detecting pseudo-commonality of job skills based on the heterogeneity level determination results and utilizing the preprocessed job requirement data, and constructing a dynamic optimization mechanism based on the pseudo-commonality detection results; a job skill mismatch structural analysis module for analyzing the degree of mismatch between individual job skills and overall skills in conjunction with the preprocessed job requirement data, and taking skill mismatch control measures based on the degree of mismatch between individual job skills and overall skills; and an intelligent visualization and feedback module for integrating job requirement data, pseudo-commonality detection results, and the degree of mismatch between individual job skills and overall skills to construct a visualization feedback mechanism, achieving a self-learning and optimization closed loop.

[0049] like Figure 4The diagram shows the workflow architecture of the job requirement information-driven survey data modeling system provided in this application embodiment, illustrating the overall working logic of the system. First, job requirement data is collected from multiple channels and preprocessed to ensure data quality and consistency. Then, pseudo-commonality and real-commonality detection is performed, using dispersion and heterogeneity indicators to identify industry commonalities and template-based illusions in job requirements. Based on this, a confidence level judgment of skill commonality is conducted to re-examine pseudo-commonality, analyzing the differences in skill requirements among different companies and determining the industry representativeness of skill items. The job requirement data and analysis results are aggregated into the job skill mismatch structural analysis module to achieve mismatch identification and case archiving. This is further broken down into two branches: skill deficiency and redundancy identification, and mismatch warning and mismatch case library, refining the mismatch types. All analysis results are displayed multidimensionally and interactively through the intelligent visualization and feedback module, supporting users' deep drilling and parameter optimization. Finally, all feedback and new data are integrated into a self-learning closed loop, continuously optimizing parameters to achieve dynamic self-evolution and intelligent decision support.

[0050] This implementation plan achieves standardized and high-quality input of job requirement information through multi-source data collection and preprocessing. It automatically identifies the heterogeneity level of job skill requirements, provides tiered early warnings of common and heterogeneous risks within the industry, effectively identifies and dynamically optimizes pseudo-commonality issues in job skills, and enhances the authenticity and representativeness of the data. Simultaneously, it conducts structural analysis of job skill mismatches, accurately assesses and adjusts the skill matching degree between jobs and talent. By constructing an intelligent visualization and feedback mechanism, it achieves full-process data monitoring, visualization of analysis results, and feedback-based self-learning and parameter optimization. This effectively improves the scientific rigor, intelligence, and dynamic adaptability of job requirement surveys.

[0051] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0052] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. As those skilled in the art will understand, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A survey data modeling method driven by job requirement information, characterized in that, Includes the following steps: S1, collect job requirement data in real time and perform data preprocessing on the job requirement data; S2, based on the preprocessed job requirement data, determine the level of heterogeneity in job skill requirements, and based on the results of the heterogeneity level determination, issue graded warnings; S3, based on the heterogeneity level discrimination results, uses preprocessed job requirement data to perform pseudo-commonality detection of job skills, and constructs a dynamic optimization mechanism based on the pseudo-commonality detection results; S4. Based on the preprocessed job requirement data, analyze the degree of mismatch between the skills of each job and the overall skills, and take skill mismatch control measures according to the degree of mismatch between the skills of each job and the overall skills. S5 integrates job requirement data, pseudo-commonality detection results, and the degree of mismatch between job skills and overall skills to build a visual feedback mechanism, achieving a self-learning and optimization loop.

2. The survey data modeling method based on job requirement information as described in claim 1, characterized in that, The specific process of real-time collection of job requirement data and data preprocessing of the job requirement data is as follows: Real-time collection of job demand data, including: job posting time, job title, industry category, job responsibilities, skill items, skill requirements text, salary, employment type, number of positions, company information, and job seeker skill text; and meta tags for each job demand data item, including collection time, company category, industry category, and geographical location. The data format of job requirement data is standardized, including unifying the core fields of job title, job responsibilities, and skill requirements. For duplicate, redundant, and invalid job requirement data, intelligent deduplication algorithms and NLP denoising techniques are used to remove noise. Job posting time and collection time are uniformly converted to a standard format. Simultaneously, job requirement data is normalized. A job requirement database is constructed to store the data. Furthermore, a professional skills ontology and a thesaurus are built based on the job requirement data. Semantic normalization is performed on synonymous skill requirement texts, practitioner skill texts, and job responsibilities, resulting in standardized mapping and tagging of job elements.

3. The survey data modeling method based on job requirement information as described in claim 1, characterized in that, The specific process of determining the heterogeneity level of job skill requirements based on the preprocessed job demand data is as follows: For the same position, the total number of companies participating in the survey is counted; the skill items for this position of a certain company are obtained and the total number of skill items is counted; the skill items for this position of each company are analyzed and compared with other companies; the number of skills that appear only in this company and not in any other company is counted as the number of unique skill items of the i-th company. The text of the job skill requirements is obtained, and the BERT model is used to process the skill requirement text, turning each skill requirement into a fixed-length numerical vector, thus obtaining the skill requirement vector. For the skill requirement vector, the Euclidean distance method is used to calculate the distance between all pairs of skill requirement vectors, and then all distances are added together and the average is calculated to obtain the average text distance of the skill requirements for the same job in the i-th company. Multiply the average text distance of the same job skill requirements in the i-th company by the distance adjustment weight factor, and add the number of unique skill items in the i-th company to obtain the composite value of job skill heterogeneity. The ratio of heterogeneity in enterprise job skills is obtained by dividing the composite value of job skill heterogeneity by the total number of skill items. Based on the total number of enterprises, calculate the proportion of heterogeneity in job skills for all enterprises, and then calculate the average value to obtain the degree of dispersion of job skill structure.

4. The survey data modeling method based on job requirement information as described in claim 1, characterized in that, The specific process for conducting graded early warning based on the heterogeneity level discrimination results is as follows: For each job category, calculate the job skill structure dispersion value and compare it with the skill dispersion thresholds T1 and T2. When the job skill structure dispersion value is less than or equal to T1, it is identified as having a false commonality risk, triggering the subsequent false commonality detection process: reviewing the confidence level of the true commonality of job skill requirements and adjusting the survey data template; optimizing the survey questionnaire and data collection rules, and filtering out templated and invalid job descriptions; When the job skill structure dispersion value is greater than T1 and less than or equal to T2, only continuous monitoring and dynamic monitoring will be maintained. When the degree of dispersion of job skill structure is greater than T2, it is identified as a risk of disordered description, and an early warning prompt for disordered description is generated. Based on historical job demand data, optimization suggestions and standardized job description templates are generated, and description specifications and standard fields are recommended. These are pushed to the data collection entry point in real time, and the templates on the data collection front end are adaptively adjusted.

5. The survey data modeling method based on job requirement information as described in claim 1, characterized in that, The specific process of detecting pseudo-commonalities in job skills using preprocessed job requirement data based on the heterogeneity level discrimination results is as follows: Receive job postings with risks of pseudo-commonality and disordered description, obtain job demand data from all companies that contain job postings with risks of pseudo-commonality and disordered description, and statistically obtain a set of companies, a set of industries, and a set of posting times. Retrieve texts of skill requirements, and count the number of companies that contain a certain skill s to obtain the set of companies where skill s appears; obtain all pseudo-commonalities and industry categories of positions with disordered risk descriptions, and retrieve and count the industry categories of industries that involve skill s to obtain the set of industries where skill s appears; Obtain the job posting time, and statistically analyze all time periods in which skill 's' appears to obtain the set of posting times for skill 's'; simultaneously Count the number of unique companies whose skill s is present in all skill requirement texts; Identify every job requirement that belongs to the enterprise set, industry set, and posting time set, and contains skill 's'. Sum the number of job requirement records to obtain the three-dimensional combination number of skill 's' occurrences. Add 1 to the number of unique enterprises for skill 's' and take the natural logarithm to obtain the enterprise distribution breadth adjustment factor. Simultaneously calculate the total number of combinations at the intersection of the entire enterprise set, industry set, and posting time set during the survey period. Multiply the three-dimensional combination number of skill 's occurrences by the enterprise distribution breadth adjustment factor to obtain the skill cross-dimensional distribution breadth value. Divide the skill cross-dimensional distribution breadth value by the total number of combinations to obtain the true commonality confidence value for job skill item 's'.

6. The survey data modeling method based on job requirement information as described in claim 1, characterized in that, The specific process of constructing the dynamic optimization mechanism based on the pseudo-commonality detection results is as follows: Calculate the true commonality confidence value for each skill requirement and compare it with the confidence threshold. When the true commonality confidence value is less than or equal to the confidence threshold, it is considered a false commonality and a templated pseudo-correlation. This skill description is immediately removed from subsequent job requirement data collection. The survey questionnaire and job description template are adjusted in real time to optimize the skill collection fields and dynamically filter and block invalid skill fields. When the confidence value of true commonality is greater than the confidence threshold, it is considered true commonality. Skills with true commonality are included in the key skills list, which drives the automatic updating of job templates, intelligent completion of job skill requirement text fields, and automatic recommendation and intelligent adaptation of job description templates, recruitment standards and personnel development paths.

7. The survey data modeling method based on job requirement information as described in claim 1, characterized in that, The specific process of analyzing the mismatch between skills for each position and overall skills by combining the preprocessed job requirement data is as follows: The survey involved the number of job positions and obtained the skill requirements texts and practitioner skill texts for each position, resulting in a set of job skill requirements and a set of practitioner skills. The number of skills that are present in the job skill requirement set but not in the practitioner's skill set is used to obtain the skill missing number. The number of skills that are present in the skill set of practitioners but not in the skill requirement set of the job is used to obtain the skill redundancy; the total number of skills is obtained by taking the union of the skill requirement set of the job and the skill set of practitioners. Multiply the number of skills redundancy by the redundancy weight factor, and add the number of skills missing to get the number of job skills mismatch. Divide the number of job skills mismatch by the total number of skills to get the job skills mismatch degree value. Based on the number of positions, the skill mismatch values ​​of all positions are summed and averaged to obtain the overall skill mismatch value.

8. The survey data modeling method based on job requirement information as described in claim 1, characterized in that, The specific process of taking skills mismatch control measures based on the degree of mismatch between the skills of each position and the overall skills is as follows: The system monitors the skill mismatch degree for each position and the overall skill mismatch degree in real time. Once the skill mismatch degree exceeds the mismatch threshold, a skill mismatch warning is generated and marked on the visualization panel. At the same time, it links with the job description template to adjust and optimize the skill requirement text fields of the job, dynamically filter redundant skill items, and accurately correct the job description. Identify skills with high mismatch rates, compile them into a list, and adaptively recommend and continuously optimize survey questionnaires, job standards, and job description templates; conduct detailed statistics on mismatch types, archive all mismatch severity values, build a mismatch case library, and periodically generate trend analysis charts and reports; We continuously collect job demand data and optimize the skill mismatch parameter.

9. The survey data modeling method based on job requirement information as described in claim 1, characterized in that, The specific process of integrating job requirement data, pseudo-commonality detection results, and the degree of mismatch between job skills and overall skills to construct a visual feedback mechanism and achieve self-learning and optimization loop is as follows: The system provides a multi-dimensional visual representation of the distribution of real and pseudo-common characteristics of job positions and the situation of skill mismatch, enabling users to flexibly filter and drill down in depth by industry, region, company, and job type. An interactive feedback mechanism is built to support online annotation, correction, and feedback of pseudo-commonality and mismatch cases by human operators, and to collect manually corrected data for parameter optimization; it generates job commonality, skill mismatch, and industry trend analysis reports, and regularly performs self-learning iterative updates on the mismatch case library and parameters to achieve a closed-loop update of intelligent analysis and optimization.

10. A survey data modeling system driven by job requirement information, characterized in that: include: The multi-source job data acquisition and preprocessing module is used to collect job requirement data in real time and perform data preprocessing on the job requirement data; The job requirement content feature modeling module is used to determine the heterogeneity level of job skill requirements based on preprocessed job requirement data, and to provide graded early warnings based on the heterogeneity level determination results. The pseudo-commonality detection and real commonality extraction module is used to perform pseudo-commonality detection of job skills based on the heterogeneity level discrimination results and using preprocessed job requirement data, and to build a dynamic optimization mechanism based on the pseudo-commonality detection results; The job skills mismatch structural analysis module is used to analyze the degree of mismatch between job skills and overall skills by combining preprocessed job requirement data, and to take skills mismatch control measures based on the degree of mismatch between job skills and overall skills. The intelligent visualization and feedback module is used to integrate job requirement data, pseudo-commonality detection results, and the degree of mismatch between job skills and overall skills to build a visual feedback mechanism and achieve a self-learning and optimization closed loop.

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