Intelligent investigation and data analysis platform and method for vocational education labor demand

Abstract

The application discloses a vocational education labor demand intelligent investigation and data analysis platform and method, and relates to the technical field of labor demand data management. The vocational education labor demand intelligent investigation and data analysis platform and method comprises the following steps: S1, collecting and preprocessing post text, context behavior and feature structure data, and constructing a standardized post text corpus dataset; S2, evaluating the semantic analysis strength of fuzzy fields, and dynamically adjusting the analysis path; S3, analyzing field mapping confidence fluctuation, and triggering context reconstruction; S4, comprehensively evaluating post credible support degree according to the semantic analysis strength and confidence fluctuation, and controlling label selection accuracy; and S5, comparing label tracks and historical attribution, identifying semantic drift and generating optimization suggestions. The problems that fuzzy expression fields cannot be identified and normalized in traditional investigation, and post ability labels cannot be accurately constructed and matched with teaching targets are solved.

Description

Technical Field

[0001] This invention relates to the field of employment demand data management technology, specifically to an intelligent survey and data analysis platform and method for employment demand in vocational education. Background Technology

[0002] With the increasingly rich structured expression of industrial activities and the continuous evolution of data processing technologies, data analysis models targeting job, task, and competency requirements are gradually shifting from manual compilation to a digital and model-based approach. In real-world scenarios, publicly available recruitment information, job descriptions, performance evaluation records, and other textual materials from companies have become crucial data sources for constructing employee demand profiles, encompassing multi-dimensional content such as job responsibilities, key competencies, behavioral requirements, and performance goals. Current analysis processes typically begin with batch collection of text content, using methods such as keyword extraction, word frequency statistics, semantic clustering, and structural tag classification to standardize the original expressions, gradually constructing a "task content - competency element" mapping relationship, and establishing a hierarchical expression model of employment demands by combining dimensions such as job level and industry attributes. Some applications also incorporate natural language processing algorithms to automatically identify and tag implicit competency requirements in descriptive statements, supporting comparative analysis and trend extraction between multiple rounds of survey data, providing a data foundation for enterprise talent selection, job design, and competency structure optimization.

[0003] For example, the invention patent with announcement number CN117035243B discloses a method and system for analyzing business demand survey reports for base station planning, relating to the field of artificial intelligence technology. In this invention, a target base station planning survey report and pending base station planning data are determined, forming a disturbed base station planning survey report for the target base station planning survey report; based on the first pending base station planning data, the disturbed base station planning survey report for the target base station planning survey report is perturbed, forming a disturb-free base station planning survey report corresponding to the first pending base station planning data; the disturb-free base station planning survey reports are merged, outputting a merged base station planning survey report; based on the merged base station planning survey reports corresponding to the pending base station planning data, the matching base station planning data for the target base station planning survey report is analyzed from the pending base station planning data. Based on the above, the reliability of the survey report analysis can be improved to a certain extent.

[0004] For example, invention patent CN110163431B discloses a method for predicting cruise passenger demand based on machine learning. This method includes: using a k-nearest neighbor algorithm based on passenger sample data to predict the travel consumption needs of potential passengers in the target group, thereby providing data support for cruise ship program design. Specific steps include: 1. Collecting sample data: Designing a survey form to collect the sample data required for machine learning. Each sample data entry includes the passenger characteristics and consumption needs of a passenger. 2. Processing sample data: Normalizing the passenger characteristics in the survey sample data one by one. 3. Using the algorithm for prediction: Inputting the characteristic data of the target group, using the k-nearest neighbor algorithm to predict the consumption needs of each passenger in the target group, and then further statistically analyzing the consumption needs of the target group, thereby providing data support for cruise ship program design. This method can provide objective, accurate, and powerful data support for cruise ship program design.

[0005] However, current methods for analyzing employment demand data still have significant limitations in dealing with vague descriptions, diverse expressions, and the integration of cross-industry capabilities. Many key expressions often use vague terms lacking standardized labels in natural language, such as "strong resilience," "rapid adaptability," and "high sense of responsibility." These fields are typically considered redundant or simplified in traditional statistical methods, lacking refined semantic parsing and structural normalization mechanisms. Furthermore, different organizations use different vocabulary and expressive styles when describing similar capability elements, leading to fragmented semantically homogeneous content in the analysis results and making it difficult to form a stable labeling system. In addition, survey data is often accumulated in batches, lacking the ability to track the evolution of fields over time, failing to identify potential drift trends in the capability demand structure and structural changes in job task semantics, thus limiting the efficiency of dynamic demand-based strategy adjustments and resource matching. To address these issues, there is an urgent need for an intelligent survey and data analysis platform and methodology for vocational education employment demand. Summary of the Invention

[0006] Technical problems to be solved

[0007] To address the shortcomings of existing technologies, this invention provides an intelligent survey and data analysis platform and method for vocational education employment needs, which solves the problems in traditional surveys that cannot identify and normalize fuzzy expression fields, making it difficult to accurately construct job competency tags and match them with teaching objectives.

[0008] Technical solution

[0009] To achieve the above objectives, this invention provides the following technical solution: a smart survey and data analysis platform and method for vocational education employment needs, comprising: S1, collecting job text data, contextual behavior data, and feature structure data during enterprise surveys, and preprocessing the collected job text data, contextual behavior data, and feature structure data to construct a standardized job text corpus dataset; S2, evaluating the semantic parsing strength of fuzzy expression fields based on the standardized job text corpus dataset, and dynamically adjusting the semantic parsing path selection based on the evaluation results; S3, analyzing the degree of confidence fluctuation of each field mapping based on the standardized job text corpus dataset and combining candidate label features, and dynamically driving context reconstruction based on the analysis results; S4, comprehensively evaluating the job credibility support of the label results using the semantic parsing strength evaluation results and confidence fluctuation analysis results as input, and driving label selection accuracy control based on the evaluation results; S5, comparing the semantic consistency of the label trajectory of the normalized field with the historical attribution results, outputting semantic drift risk items and generating optimization suggestions.

[0010] Furthermore, the specific steps for collecting job text data, contextual behavior data, and feature structure data during the enterprise survey process, and preprocessing the collected job text data, contextual behavior data, and feature structure data to construct a standardized job text corpus dataset are as follows: Collect job text data generated during the enterprise survey process. This job text data includes: the number of competency keywords, semantic concentration coefficient, and normalized tag call frequency. Simultaneously, it records the total number of normalized tags in the current job description, the total frequency of normalized tag calls, and the number of candidate tag sets corresponding to each field. Collect contextual behavior data related to expression ambiguity. This contextual behavior data includes: the subject-predicate structure type of the statement containing the field and semantic similarity, and calculates... Furthermore, it records the average semantic bias value, average semantic similarity, and the number of times each field was normalized to different labels in multi-job surveys for all fields under the same job category; it collects feature structure data during the survey process, including: the total length of each expression field, the standard deviation of confidence in the field normalization process, and the maximum standard deviation of confidence in all fields; it standardizes and normalizes the collected job text data, context behavior data, and feature structure data to correct semantic ambiguity caused by differences in industry terminology, and archives and stores the standardized and normalized job text data, context behavior data, and feature structure data in a unified manner to construct a standardized job text corpus dataset.

[0011] Furthermore, the specific steps for evaluating the semantic parsing strength of fuzzy expression fields based on the standardized job text corpus dataset are as follows: The expression fields in the standardized job text corpus dataset are extracted using the dependency parsing tool LTP to obtain the number of verb-object pairs. Simultaneously, modifiers are extracted, and the distribution deviation of modifier positions in the fields is calculated to obtain the standard deviation of modifiers. The total length of the expression fields in the standardized job text corpus dataset is extracted. The total length of the expression fields and the standard deviation of modifiers are multiplied and then divided by the number of verb-object pairs in the expression fields plus one to obtain the basic structural complexity. The number of capability keywords appearing in the fields is counted and divided by the total field length to obtain the capability feature density. The square of the capability feature density is calculated, and then one is added to obtain the pairwise result. The initial semantic deconstruction degree is obtained by adding the basic structural complexity to the initial semantic deconstruction degree. The similarity distribution of fields in the label semantic space is calculated using a word embedding model combined with a domain corpus classification model, yielding the contextual semantic bias value of each field's corresponding job position. Simultaneously, the average semantic bias value of all fields within the same job category is calculated. The absolute value of the difference between the contextual semantic bias value and the average semantic bias value is calculated, divided by the average semantic bias value, and then incremented by one to obtain the attribution semantic deviation ratio. The number of divergences in the current field's classification to different labels in a multi-job survey is extracted, and the number of divergences is divided by the total number of classified labels in the current job description to obtain the high-frequency divergence rate. The initial semantic deconstruction degree is multiplied by the attribution semantic deviation ratio, and then the high-frequency divergence rate is subtracted to obtain the semantic parsing strength value.

[0012] Furthermore, the specific steps for dynamically adjusting the semantic parsing path selection based on the evaluation results are as follows: Real-time comparison of the semantic parsing strength value of the current fuzzy expression field with the semantic parsing recognition threshold: When the semantic parsing strength value is less than or equal to the semantic parsing recognition threshold, it is determined to be a segment with a clear semantic structure. The original semantic parsing path and normalized label allocation strategy remain unchanged, and the field standardization process and capability label mapping mechanism continue to operate without needing to perform parsing rule reconstruction or intervention labeling operations, maintaining the normalized module processing channel's normalized operation. When the semantic parsing strength value is greater than the semantic parsing recognition threshold, it is determined to be a segment with a complex semantic structure, entering the high-complexity expression parsing process: Immediately triggering the context reconstruction mechanism, prioritizing the extraction of behavioral guidance words from the preceding and following sentences in the segment where the field is located, simultaneously starting the label multi-matching channel, shortening the backtracking window length of the field parsing channel, and increasing the drift detection frequency.

[0013] Furthermore, the specific steps for analyzing the confidence fluctuation of each field's mapping based on the standardized job text corpus dataset and candidate label features are as follows: Extract the semantic similarity of all candidate labels within the normalization process for the current field; calculate the difference between the semantic similarity of the current candidate label "steel ball" and the average semantic similarity of all candidate labels, divide by the average semantic similarity, and then square the result to obtain the candidate semantic clustering deviation; sum the candidate semantic clustering deviations of all candidate labels and divide by the number of candidate label sets corresponding to the current field to obtain the semantic clustering deviation mean square value; statistically analyze the current character... The number of times each label in a segment is shared by multiple fields is recorded as the label's normalized coverage. The label normalization path of all fields under the job position is traversed, the position difference of the fields corresponding to all labels is calculated, and then the average is taken to obtain the label's average span. The normalized coverage is divided by the label's average span, and then multiplied by the ratio between the standard deviation of confidence and the maximum standard deviation of confidence in the field normalization mapping process plus one to obtain the normalized attribute multi-source correction value. The normalized attribute multi-source correction value is multiplied by the corresponding normalization mapping adjustment factor and then added to the semantic clustering deviation mean square value to obtain the normalized confidence fluctuation value of the current field.

[0014] Furthermore, the specific steps of dynamically driving context reconstruction based on the analysis results are as follows: When the normalized confidence fluctuation value shows an increasing trend in three consecutive fields, and the increase gradually expands, it is determined that the current field is in a high uncertainty accumulation interval. The context reconstruction mechanism is immediately activated, the paragraph to which the field belongs is expanded one or two sentences before and after, the combination of behavior-oriented words and ability-modifying words is re-analyzed, and the original expression and context combination are uniformly processed as a whole to construct a semantically enhanced label structure, and the field is recorded as an expression to be stabilized; When the normalized confidence fluctuation value fluctuates frequently between multiple fields, and the increase in the difference between the upper and lower values ​​is obvious, it is determined to be a semantic transition interval. The field is simultaneously labeled with the main label and the auxiliary label. All candidate labels and their semantic associations are retained by establishing a one-to-many label reference table; When the normalized confidence fluctuation value has a very small fluctuation range and a concentrated value range in multiple consecutive fields, it is determined that the current normalization state is in a semantic convergence interval. Then, the candidate label with the highest similarity is selected as the normalization label of the field, and the mapping path is marked as unidirectionally stable and included in the standard normalization result library.

[0015] Furthermore, the specific steps for comprehensively evaluating the job credibility support of the label results using the semantic parsing strength assessment results and the confidence fluctuation analysis results as input are as follows: Obtain the semantic parsing strength value and the normalized confidence fluctuation value; divide the semantic parsing strength value by the value after adding one to the semantic parsing strength value, and then multiply it by the value after adding one to the semantic concentration coefficient and taking the logarithm to obtain the concentration complexity; statistically analyze the frequency of normalized label calls for all normalized labels in the job field, calculate the standard deviation, and normalize it to obtain the job label smoothness; divide the current normalized label call frequency by the sum of the total frequency of normalized label calls and the job label smoothness to obtain the label support; divide one by the value after adding one to the normalized confidence fluctuation value and then add one to obtain the stability correction value; multiply the concentration complexity, label support, and stability correction value to obtain the job semantic stability value.

[0016] Furthermore, the specific steps for controlling the label selection accuracy based on the evaluation results are as follows: Real-time comparison of the job semantic stability value with the label construction stability grading threshold, which includes a first stability threshold and a second stability threshold: When the job semantic stability value is less than or equal to the second stability threshold, it is determined to be a low-confidence segment. The semantic parsing structure of the field and the job task context are cached and archived to reduce the frequency of normalization recalculation and release computing resources to support subsequent field label generation tasks; When the job semantic stability value is greater than the second stability threshold and less than or equal to the first stability threshold, it is determined to be a medium-confidence segment. Dynamic structure mapping is enabled, the candidate label sorting strategy is adjusted, and reversible adjustment permissions are retained; When the job semantic stability value exceeds the first stability threshold, it is determined to be a high-confidence segment. Fields in the current window no longer participate in label negotiation and structure reconstruction tasks and enter a frozen state. Simultaneously, a horizontal comparison process between the label vector and the course target library is triggered.

[0017] Furthermore, the specific steps for comparing the semantic consistency of the label trajectory of the normalized field with the historical attribution results, outputting semantic drift risk items, and generating optimization suggestions are as follows: record the correspondence between each normalized field and the standard label, perform a longitudinal comparison of each normalization result, and when the same field is normalized to multiple labels in different batches, mark it as a semantic drift risk item and include it in the tracking sequence. At the same time, call the calculation results of the normalization confidence fluctuation value and the semantic parsing strength value to evaluate whether there are high-frequency changes in the label normalization path and whether the candidate labels are frequently replaced. Combined with the label clustering trend of the job category, determine whether the field has a stable semantic attribution, output the normalization quality level, and generate a change log of the job competency profile. Optimization suggestions are output for the concentrated sections of semantic drift risk items, fields with frequent normalization changes, and areas with uneven competency distribution to help personnel identify blind spots in course coverage.

[0018] The second aspect of this invention provides an intelligent survey and data analysis platform for vocational education employment needs, comprising: a data acquisition and preprocessing module, used to collect job text data, contextual behavior data, and feature structure data during enterprise surveys, and preprocess the collected job text data, contextual behavior data, and feature structure data to construct a standardized job text corpus dataset; an expression field semantic parsing module, used to evaluate the semantic parsing strength of fuzzy expression fields based on the standardized job text corpus dataset, and dynamically adjust the semantic parsing path selection based on the evaluation results; an expression field normalization mapping module, used to analyze the degree of confidence fluctuation of each field mapping based on the standardized job text corpus dataset and candidate label features, and dynamically drive context reconstruction based on the analysis results; a job competency label construction module, used to comprehensively evaluate the job credibility support of the label results with the semantic parsing strength evaluation results and confidence fluctuation analysis results as input, and drive label selection precision control based on the evaluation results; and a competency semantic feedback and verification module, used to compare the semantic consistency of the label trajectory of the normalized field with the historical attribution results, output semantic drift risk items, and generate optimization suggestions.

[0019] Beneficial effects

[0020] The present invention has the following beneficial effects:

[0021] (1) The intelligent survey and data analysis platform and method for vocational education employment demand, by constructing a joint analysis mechanism of semantic parsing strength value and normalized confidence fluctuation value, dynamically identifies fuzzy expression fields and normalized stability levels in survey texts, effectively solving the problems of lack of structured attribution of fuzzy terms and insufficient granularity of analysis results in traditional surveys.

[0022] (2) The intelligent survey and data analysis platform and method for vocational education employment demand introduces the job semantic stability value as the evaluation index of the normalization path, realizes the automatic tracking and consistency verification of the label affiliation changes in multiple rounds of survey data, solves the technical problem that the existing methods cannot perceive the normalization change trend and the stability of the ability labels, and improves the reliability of course matching analysis.

[0023] (3) The intelligent survey and data analysis platform and method for vocational education employment demand, by integrating semantic triples and unified ability tags in the original task text of the job, constructs a tag calling structure of task segment-ability item, which solves the problems of the extraction of ability items relying on manual summarization and the fuzzy mapping between job tasks and ability requirements in the existing demand analysis, and realizes the structured expression of job task ability and the support of course ability points.

[0024] (4) The intelligent survey and data analysis platform and method for vocational education employment demand, by constructing a normalized field version tracking and semantic change chain, combined with the label clustering trend and label change frequency of job categories, automatically evaluates the semantic normalization quality level, solves the problems of inconsistent label attribution and chaotic course adaptation logic between different survey batches, and strengthens the traceability of course updates and the dynamic stability of the normalization mechanism.

[0025] 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

[0026] Figure 1 This is a flowchart of the intelligent survey and data analysis method for vocational education employment demand in this invention;

[0027] Figure 2 This is a structural diagram of the intelligent survey and data analysis platform for vocational education employment needs of the present invention;

[0028] Figure 3 This is a line graph showing the semantic stability values ​​of the job positions involved in this invention.

[0029] Figure 4 This is a flowchart of the fuzzy expression field normalization process involved in this invention. Detailed Implementation

[0030] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only 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.

[0031] Please see Figures 1-4This invention provides a technical solution: an intelligent survey and data analysis platform and method for vocational education employment needs, comprising: S1, collecting job text data, contextual behavior data, and feature structure data during enterprise surveys, and preprocessing the collected job text data, contextual behavior data, and feature structure data to construct a standardized job text corpus dataset; S2, evaluating the semantic parsing strength of fuzzy expression fields based on the standardized job text corpus dataset, and dynamically adjusting the semantic parsing path selection based on the evaluation results; S3, analyzing the degree of confidence fluctuation of each field mapping based on the standardized job text corpus dataset and combining candidate label features, and dynamically driving context reconstruction based on the analysis results; S4, comprehensively evaluating the job credibility support of the label results using the semantic parsing strength evaluation results and confidence fluctuation analysis results as input, and driving label selection accuracy control based on the evaluation results; S5, comparing the semantic consistency of the label trajectory of the normalized field with the historical attribution results, outputting semantic drift risk items and generating optimization suggestions.

[0032] Specifically, the process involves collecting job description text data, contextual behavior data, and feature structure data during enterprise surveys. Preprocessing this data to construct a standardized job description text corpus dataset involves the following steps: First, collecting job description text data generated during enterprise surveys, including job descriptions, recruitment announcements, and job task descriptions. Natural language processing tools are used for word segmentation, part-of-speech tagging, and syntactic analysis to extract capability keywords from each expression field and count their quantity. Simultaneously, a semantic embedding model is used to model the matching relationship between fields and tags, obtaining a semantic concentration coefficient to measure the degree of semantic focus. Then, combined with the call records of the tag normalization process, the call frequency of each tag in the current job description is counted as a normalized tag call frequency indicator. Furthermore, through a tag mapping database, the total number of normalized tags in the current job description, the total call frequency of normalized tags for all fields within the job task segment, and the number of candidate tag sets corresponding to each field are further counted.

[0033] To collect contextual behavioral data related to ambiguity, the process begins by identifying the set of modifiers in each field using a modifier extraction algorithm and calculating the standard deviation of this set to assess the dispersion of language modification. Next, a semantic representation vector is constructed based on a context embedding model, and the semantic similarity score between each field and its candidate labels is calculated, with the range of variation recorded. Simultaneously, based on a job classification system, the current job is categorized, and the average semantic bias and average semantic similarity of all fields within that category are calculated to assess the level of semantic consistency. Finally, to measure the stability of label normalization in the survey, the number of divergences in different job categories for each field is statistically analyzed, forming a field-level normalization ambiguity index.

[0034] The process of collecting feature structure data during the survey includes extracting the total text length of each expression field and the number of verb-object pairs in the syntactic structure through grammatical analysis to assess structural complexity. Simultaneously, during the job normalization mapping process, a label semantic mapping model such as a confidence matching network and a manual scoring mechanism are invoked to record the confidence value of the candidate label corresponding to each field and calculate the standard deviation of the confidence to reflect the matching stability. Finally, all fields are traversed, the standard deviation of their normalized confidence sequence is extracted, and the maximum value is determined as a reference value for the upper limit of normalization uncertainty.

[0035] The collected job text data, contextual behavior data, and feature structure data are standardized and normalized. Z-score standardization and Min-Max normalization algorithms are used to eliminate differences in field scale. At the same time, a domain dictionary and a thesaurus are introduced to align terms and correct semantic ambiguity and mapping inconsistencies caused by differences in industry terminology. The processed data are archived in a unified manner to construct a multi-dimensional, cross-searchable standardized job text corpus dataset.

[0036] This implementation plan integrates key factors of content expression, semantic association, and structural features by collecting and standardizing job text data, contextual behavior data, and feature structure data in multiple dimensions. It effectively solves the data inconsistency problem caused by terminology differences, expression ambiguity, and semantic drift, and provides high-quality, comparable, and adaptable basic data support for subsequent intelligent analysis tasks such as job competency profiling, label normalization modeling, and risk assessment.

[0037] Specifically, based on a standardized job text corpus dataset, the steps for evaluating the semantic parsing strength of fuzzy expression fields are as follows: The expression fields in the standardized job text corpus dataset are extracted using the dependency parsing tool LTP to obtain the number of verb-object pairs. Simultaneously, modifiers are extracted, and the distribution deviation of modifier occurrences in the fields is calculated to obtain the standard deviation of modifiers. The total length of the expression fields in the standardized job text corpus dataset is extracted. The total length of the expression fields is multiplied by the standard deviation of modifiers and then divided by the number of verb-object pairs in the expression fields plus one to obtain the basic structural complexity. The number of capability keywords appearing in the fields is counted and divided by the total field length to obtain the capability feature density. The square of the capability feature density is calculated, and then the logarithm is taken after adding one. Adding this to the basic structural complexity yields the preliminary semantic deconstruction degree. The similarity distribution of fields in the label semantic space is calculated using a word embedding model combined with a domain corpus classification model, yielding the contextual semantic bias value for each field's corresponding job position. Simultaneously, the average semantic bias value for all fields within the same job category is calculated. The absolute value of the difference between the contextual semantic bias value and the average semantic bias value is calculated, divided by the average semantic bias value, and then incremented by one to obtain the attribution semantic deviation ratio. The number of divergences where the current field is assigned to different labels in a multi-job survey is extracted. This number of divergences is divided by the total number of attribution labels in the current job description to obtain the high-frequency divergence rate. Finally, the preliminary semantic deconstruction degree is multiplied by the attribution semantic deviation ratio, and the high-frequency divergence rate is subtracted to obtain the semantic parsing strength value.

[0038] The formula for calculating the semantic parsing strength value is:

[0039] ;

[0040] In the formula, This indicates the total length of the expression field, which is used to measure the text complexity of the field and is a basic indicator for evaluating the parsing workload. It is derived from the number of characters in this field in the standardized job text corpus dataset. The standard deviation of modifiers is used to quantify the dispersion of the types and distribution of modifiers in a field. It is a key indicator for assessing the diversity of modifiers. The source is the number of characters in this field in the standardized job text corpus dataset. Capability feature density is used to quantify the number of capability keywords appearing in a unit length of text. It is a core indicator for evaluating the richness of field capability information. Capability keywords appearing in the matching field text are obtained, and the number of capability keywords is divided by the total length of the field to obtain the capability feature density. This indicates the number of verb-object pairs in the expression field, used to measure the completeness of the main semantic structure and serving as a fundamental basis for judging the strength of syntactic support. Based on dependency parsing tools, verb-object dependency pairs are extracted, and the frequency of occurrence of such relationships is statistically analyzed. This represents the contextual semantic bias value of the current field's job title. It is used to quantify the semantic tendency of the field in the industry context and serves as a reference indicator for identifying domain specificity. The semantic vector of the field is obtained through a word embedding model, and the cosine similarity between the field and the aggregated vector of the context segment of the job title is calculated as the bias value. It represents the average semantic bias value of all fields under the same job category, used to measure the overall semantic central tendency, and serves as a benchmark for normalized mapping calibration; This indicates the number of divergences in which the current field is classified into different labels in a multi-position survey. It is used to quantify the risk of semantic drift and is a sensitive indicator for determining the stability of labels. This represents the total number of normalized tags in the current job description. It is used to measure the diversity of the job's required skills and is an auxiliary indicator for interpreting the density of tag distribution.

[0041] In this implementation plan, the formula is used to comprehensively measure the semantic construction complexity, semantic deviation, and multi-source normalization uncertainty of a field in the job context, in order to achieve a quantitative assessment of the degree of expression ambiguity. By integrating four key elements—basic structural complexity, capability feature density, contextual semantic deviation ratio, and normalization divergence risk—the semantic parsing strength value can serve as the core control basis in the field normalization process: when the field semantics are clear and the normalization is stable, the semantic parsing strength value is low, which can maintain the original label allocation and normalization path; when the field semantic construction is complex, the attribution is ambiguous, or the normalization result fluctuates greatly, the semantic parsing strength value increases, which can trigger the parsing strategy adjustment mechanism of context reconstruction and label rematching. Therefore, the semantic parsing strength value formula is the core calculation method for determining whether a field enters the high-complexity expression parsing process, and it is a key supporting link in the semantic normalization intelligent control process.

[0042] Specifically, the steps for dynamically adjusting the semantic parsing path selection based on the evaluation results are as follows: Real-time comparison of the semantic parsing strength value of the current fuzzy expression field with the set semantic parsing recognition threshold: When the semantic parsing strength value is less than or equal to the threshold, it is identified as a semantically clear segment. At this time, the current field parsing path remains unchanged, the original label normalization process is followed, the established semantic mapping relationship and capability item index table are continuously called, and field distribution and label calling instructions are executed. The normalization processing module runs stably according to the default scheduling order, without triggering path correction, label comparison update or intervention prompt push operation, to ensure the efficient flow of the processing link and the stability of label continuation.

[0043] When the semantic parsing strength value exceeds the recognition threshold, it is identified as a segment with complex semantic structure, and immediately switches to high-complexity parsing logic: reconstruct the contextual expression path of the paragraph where the field is located, identify instructional expressions and behavioral guide words within the same block, update the priority of the field parsing starting point and trigger words; start a multi-label matching queue in parallel to enhance the flexibility of candidate label selection; at the same time, compress the sliding span of the field backtracking window, improve the response density of historical expression comparison, increase the detection frequency of label drift trend, and provide a basis for judgment for subsequent normalized label reconstruction and tracking strategy updates.

[0044] In this implementation scheme, the complexity of the semantic expression of a field is perceived in real time during the normalization process. By continuously comparing the semantic parsing strength value of the current field with the set semantic parsing recognition threshold, it dynamically determines whether the field has a stable semantic structure. When the semantic parsing strength value is less than or equal to the recognition threshold, the semantic structure of the field is determined to be clear. The original label normalization process is directly followed, calling the existing semantic mapping relationship and capability item index table to perform field distribution and label calling operations. The normalization processing link runs stably according to the default scheduling order without triggering path correction, label comparison update, or intervention prompt push, ensuring the efficient flow and continuity of results in the label allocation process. When the semantic parsing strength value is greater than the recognition threshold, the semantic structure of the field is determined to be complex. The high-complexity expression parsing logic is immediately switched, the context expression path of the paragraph where the field is located is reconstructed, the instruction expression and behavior guide words in adjacent sentences are extracted, and the field parsing starting point and trigger word calling order are updated. A multi-label matching process is started in parallel to improve the elasticity of candidate label calling. The sliding span of the field backtracking window is compressed to increase the frequency of historical expression comparison and the recognition density of label drift trends, providing a basis for judgment for label normalization reconstruction and expression path update. This mechanism enables the field parsing path to proactively respond to the complexity of the expression, effectively improving the adaptability and robustness of the normalization processing link in ambiguous segments.

[0045] Specifically, based on a standardized job posting text corpus dataset, and combined with candidate label features, the analysis of the confidence fluctuation of each field mapping involves the following steps: extracting the semantic similarity of all candidate labels within the normalization process for the current field; calculating the difference between the semantic similarity of the current candidate label "steel ball" and the average semantic similarity of all candidate labels, dividing by the average semantic similarity, and then squaring the result to obtain the candidate semantic clustering deviation; summing the candidate semantic clustering deviations of all candidate labels and dividing by the number of candidate label sets corresponding to the current field to obtain the mean square value of semantic clustering deviation; and statistically analyzing the current field... The number of times each label is shared by multiple fields is recorded as the label's normalized coverage. The label normalization path of all fields under the job position is traversed, the position difference of the fields corresponding to all labels is calculated, and then the average is taken to obtain the label average span. The normalized coverage is divided by the label average span, and then multiplied by the ratio between the standard deviation of confidence and the maximum standard deviation of confidence in the field normalization mapping process plus one to obtain the normalized attribute multi-source correction value. The normalized attribute multi-source correction value is multiplied by the corresponding normalization mapping adjustment factor and then added to the semantic clustering deviation mean square value to obtain the normalized confidence fluctuation value of the current field.

[0046] The formula for calculating the normalized confidence fluctuation value is:

[0047] ;

[0048] In the formula, This represents the number of candidate label sets corresponding to a field, used to count the number of synonym clustering paths during the normalization process. This represents the semantic similarity score of the j-th candidate label in the field, reflecting its proximity to the semantic center of the target label; This represents the average semantic similarity of all candidate labels within a field, used to measure the semantic consistency of that field during the normalization process; This represents the normalized coverage number of a field that is shared by multiple labels, and is used to measure the one-to-many attribution tendency of a field. The average span of tags representing the job category to which the field belongs reflects the breadth of its semantic adaptation domain; This represents the standard deviation of the confidence level during the field normalization mapping process, used to reflect the fluctuation range of the mapping corresponding to different labels; This represents the maximum standard deviation of the confidence level across all fields, used for normalization of the results. This represents the normalization mapping adjustment factor, ranging from 0.1 to 1.5. It controls the weight of semantic consistency bias and multi-source normalization interference in the final confidence fluctuation value. Its source is based on the normalization stability score and label distribution balance index of the current normalized field in the job description scenario. The specific acquisition method is as follows: First, extract the confidence score sequence of normalized labels for each batch during multiple rounds of job surveys, calculate its average confidence score and standard deviation, and further match it with the normalization threshold sequence to obtain its normalization mapping stability score. Then, combine the number of normalized labels in the label normalization path, the overlap rate of the normalization path, and the number of normalized label divergences to assess the degree of multi-label distribution conflict in the normalization path. Subsequently, construct a normalization adjustment factor function between the stability score and the path conflict degree. By balancing the influence of these two factors, determine the degree to which confidence bias suppression needs to be strengthened during the semantic parsing of this field. If the confidence level of the normalization label determination process for this field fluctuates significantly and the label coverage path is chaotic, the λ value is relatively increased to enhance the sensitivity to imbalances in normalization consistency. If the field label calls are concentrated and the normalization label determination tends to be stable, the λ value is appropriately reduced to maintain the generalization ability of label matching and the convergence of the normalization logic. Ultimately, by dynamically adjusting the λ value, adaptive adjustment of semantic consistency analysis and multi-source label normalization conflicts can be achieved, thereby optimizing the confidence stability of the normalization mapping and the convergence efficiency of the label selection path.

[0049] This implementation plan comprehensively evaluates the degree of semantic consistency deviation, multi-source label normalization interference, and mapping confidence stability of the job field during the label normalization process to determine the stability and reliability of the field normalization results. The first term on the left side of the formula quantifies the dispersion of semantic aggregation during the normalization process by statistically analyzing the deviation of candidate label semantic similarity scores from their average value. The weighted term on the right side combines the label normalization coverage and the average label span to measure the tendency of multi-label sharing and semantic cross-domain complexity of the field. It also introduces the ratio of the normalization confidence standard deviation to its maximum standard deviation to reflect the significance of confidence fluctuations corresponding to different labels. The normalization mapping adjustment factor λ is used to dynamically control the weights of the aforementioned multi-source interference factors, improving the adaptability of the normalization process to changes in label determination stability and semantic concentration. Overall, this formula, through multi-factor fusion calculation, outputs a comprehensive value of confidence fluctuations during the field normalization process, providing a reliable basis for subsequent label path correction, normalization strategy selection, and semantic parsing complexity assessment.

[0050] Specifically, the steps for dynamically driving context reconstruction based on the analysis results are as follows: Figure 4The flowchart for fuzzy expression field normalization is shown. Based on the fluctuation intensity of the normalized confidence fluctuation value, the field is divided into intervals and corresponding follow-up measures are taken: When the normalized confidence fluctuation value shows an increasing trend in three consecutive fields, and the increase gradually expands, it indicates that the semantic consistency of the field has decreased and the label pointing divergence has increased during label normalization, indicating that it is currently in a high uncertainty accumulation interval. At this time, the context reconstruction mechanism is immediately activated, expanding the paragraph to which the field belongs by one or two sentences before and after. The main semantic chain and boundary modification fragments are extracted from the expanded paragraph, focusing on identifying the combination relationship between behavior-oriented words and ability-modifying words, and establishing a chunk structure diagram. The chunk structure is then fused and normalized with the original field expression to generate a semantically enhanced label structure with contextual semantic support, significantly improving the stability and contextual adaptability of label selection. This field is then marked as an expression to be stabilized and enters the subsequent re-evaluation sequence.

[0051] When the normalized confidence fluctuation value fluctuates frequently across multiple fields, and the increase in the difference between the upper and lower values ​​is significant, it reflects the problem of ambiguous semantic coverage boundaries and frequent jumps in the normalization path, and is identified as a semantic transition interval. For this type of field, a label fuzzy band is constructed, with the main label set as the core identifier, while retaining auxiliary labels with high semantic similarity among the candidate labels. By establishing a one-to-many label reference table, all candidate labels and their semantic correlation information with fields are recorded in a structured manner, enhancing the flexibility and traceability of label decision-making.

[0052] When the normalized confidence fluctuation value exhibits minimal fluctuations and concentrated values ​​across multiple consecutive fields, it indicates that the field's semantic expression is clear and the labels are highly consistent, suggesting that the current normalization state is within the semantic convergence range. In this case, the label with the highest semantic similarity score is directly selected from the candidate labels as the field's normalized label. Simultaneously, the label mapping path is marked as a unidirectional stable mapping path and added to the standard normalization result library for path reuse and label recommendation in subsequent normalization processes. This process compresses unnecessary label comparison steps while ensuring processing stability, improving overall normalization processing efficiency and label matching consistency.

[0053] This implementation scheme dynamically identifies semantic uncertainty states during the normalization process and implements differentiated processing strategies based on the changing trends of normalization confidence fluctuation values. This enhances the adaptability of label normalization under different representational complexities and the accuracy of label assignment. By triggering context reconstruction in high uncertainty accumulation intervals, constructing label fuzzy bands in semantic transition intervals, and performing stable normalization mapping in semantic convergence intervals, intelligent diversion of the normalization strategy and flexible adaptation of the label structure can be achieved. This effectively avoids problems such as label drift, normalization path interruption, and normalization misjudgment, enhancing the robustness and semantic stability of the overall label normalization mechanism.

[0054] Specifically, using the semantic parsing strength assessment results and confidence fluctuation analysis results as inputs, the comprehensive evaluation of the job credibility support of the label results involves the following steps: obtaining the semantic parsing strength value and the normalized confidence fluctuation value; dividing the semantic parsing strength value by the sum of the semantic parsing strength value and the normalized confidence fluctuation value, and then multiplying it by the logarithm of the semantic concentration coefficient plus one to obtain the concentration complexity; statistically analyzing the frequency of normalized label calls in the job field for all normalized labels and calculating the standard deviation, then normalizing it to obtain the job label smoothness; dividing the current normalized label call frequency by the sum of the total normalized label call frequency and the job label smoothness to obtain the label support; dividing one by the sum of the normalized confidence fluctuation value and the normalized confidence fluctuation value, and then adding one to obtain the stability correction value; and multiplying the concentration complexity, label support, and stability correction value to obtain the job semantic stability value.

[0055] The formula for calculating the semantic stability value of a job is:

[0056] ;

[0057] In the formula, S represents the semantic parsing strength value, which is used to quantify the semantic structure complexity, modifier superposition degree and parsing path stability of fuzzy fields, and is the basic factor for judging the controllability of field parsing; The semantic concentration coefficient represents the set of candidate labels, which reflects the degree of aggregation of expressions among similar labels and is a trade-off parameter for the quality of semantic equivalence mapping. This indicates the frequency of the current normalized label call, which is an indicator of the ability item's participation in task support; This indicates the total frequency of normalized label calls within a job task segment, used to standardize label importance. R represents the smoothness of job labels, used to measure vector fluctuations caused by differences in job task granularity; R represents the normalized confidence fluctuation value, used to measure the confidence fluctuation range and semantic uncertainty of the label attribution path, and is a key parameter for constructing a label stability mapping strategy.

[0058] In this implementation example, the semantic parsing strength value of Example 1 is set to 3.6, the semantic concentration coefficient is set to 2.1, the current normalized label call frequency is set to 15, the total normalized label call frequency is set to 28, the job label smoothness is set to 3.0, and the normalized confidence fluctuation value is set to 2.5.

[0059] Example 2 sets the semantic parsing strength value to 2.4, the semantic concentration coefficient to 1.8, the current normalized label call frequency to 12, the total normalized label call frequency to 30, the job label smoothness to 2.5, and the normalized confidence fluctuation value to 1.9.

[0060] Example 3 sets the semantic parsing strength value to 4.8, the semantic concentration coefficient to 2.7, the current normalized label call frequency to 18, the total normalized label call frequency to 35, the job label smoothness to 2.8, and the normalized confidence fluctuation value to 3.1.

[0061] Example 4 sets the semantic parsing strength value to 1.9, the semantic concentration coefficient to 1.5, the current normalized label call frequency to 9, the total normalized label call frequency to 22, the job label smoothness to 2.2, and the normalized confidence fluctuation value to 1.6.

[0062] Example 5 sets the semantic parsing strength value to 3.2, the semantic concentration coefficient to 2.3, the current normalized label call frequency to 17, the total normalized label call frequency to 27, the job label smoothness to 2.9, and the normalized confidence fluctuation value to 2.7.

[0063] Example 6 sets the semantic parsing strength value to 2.7, the semantic concentration coefficient to 1.9, the current normalized label call frequency to 13, the total normalized label call frequency to 26, the job label smoothness to 2.6, and the normalized confidence fluctuation value to 1.8.

[0064] In Example 7, the semantic parsing strength value is set to 4.0, the semantic concentration coefficient is set to 2.5, the current normalized label call frequency is set to 16, the total normalized label call frequency is set to 33, the job label smoothness is set to 3.1, and the normalized confidence fluctuation value is set to 2.2. The job semantic stability value for each example is calculated, as shown in Table 1, the job semantic stability value data table.

[0065] Instance number Semantic parsing strength value Semantic concentration coefficient Current frequency of normalization tag calls Total frequency of normalized tag calls Job label smoothness Normalized confidence fluctuation value Job semantic stability value Example 1 3.6 2.1 15 28 3.0 2.5 2.951 Example 2 2.4 1.8 12 30 2.5 1.9 2.421 Example 3 4.8 2.7 18 35 2.8 3.1 3.786 Example 4 1.9 1.5 9 22 2.2 1.6 1.923 Example 5 3.2 2.3 17 27 2.9 2.7 3.152 Example 6 2.7 1.9 13 26 2.6 1.8 2.583 Example 7 4.0 2.5 16 33 3.1 2.2 3.453

[0066] like Figure 3 As shown in Table 1, this is a line graph of the semantic stability value of the job provided in this application example. Figure 3As can be seen, the job semantic stability value of Example 3 is the highest, reflecting its strong semantic parsing strength, clear label calling behavior, and good control of normalization confidence fluctuations. This indicates that the semantic structure of this field is clear, the label support is high, and the fluctuation stability is strong, making it suitable as a priority object in the semantic normalization process, which helps improve the accuracy of label mapping and the coherence of the normalization link. In contrast, the job semantic stability value of Example 4 is the lowest. Although the semantic concentration coefficient is low, the frequency of normalization label calls and the fluctuation value show a weak synchronous response, resulting in insufficient parsing stability in the semantic normalization process. Therefore, its priority in the label calling order will be automatically reduced, and it will be reserved as an auxiliary labeling object to reduce the risk of incorrect mapping and unnecessary intervention resource consumption. The line graph of job semantic stability value can intuitively show the difference in stability of each field in the normalization parsing task. The higher the evaluation value, the more concentrated the semantics of the field and the more robust the label normalization. It is more suitable for priority calling in a high-consistency label strategy to ensure the accuracy and consistency of semantic mapping.

[0067] Specifically, the steps for controlling the accuracy of label selection based on the evaluation results are as follows: Real-time comparison of the semantic stability value of the job position with the label to construct a stability grading threshold, which consists of a first stability threshold and a second stability threshold.

[0068] When the semantic stability value of a job position is less than or equal to the second stability threshold, it is identified as a low-confidence segment. Such fields typically exhibit characteristics such as large fluctuations in the semantic parsing path, high divergence rates in candidate labels, or insufficient contextual semantic support. In this case, the semantic parsing structure of the field, along with its associated job task segment, is immediately cached and archived, and the processing priority is adjusted to reduce the recalculation frequency of this field in the normalized channel, avoiding resource waste. Simultaneously, computing resources are released to provide processing space for subsequent field label construction and semantic mapping, ensuring overall processing efficiency.

[0069] When the semantic stability value of a job position is greater than the second stability threshold but not exceeding the first stability threshold, it is determined to be a medium-confidence segment. Such fields have a certain degree of semantic coherence and label support, but there are problems such as unstable structural hierarchy and the possibility of regression in the normalization path. At this time, the dynamic structure mapping logic is activated to dynamically adjust the sorting priority of candidate labels in the matching sequence to enhance the label normalization fit, while retaining the permissions for path regression and label switching, providing adjustment space for subsequent semantic evolution.

[0070] When the semantic stability value of a job position is greater than the first stability threshold, it is marked as a high-confidence segment, indicating that the expression structure of the field is highly stable, the normalization mapping path is clear and explicit, and the tag call records are dense and consistent. At this time, the tag status of the current field should be frozen immediately, and its participation in the tag negotiation and structural reconstruction task should be terminated. At the same time, the horizontal comparison process between the tag vector and the course target vocabulary should be initiated to verify the ability association and confirm the course mapping of the tag normalization results, thereby improving the consistency and stability of the normalized tags in subsequent teaching resource matching and ability model construction.

[0071] In this implementation plan, the semantic stability value of the job position is compared with the stability grading threshold of the tag construction in real time, and the trust segment in which the current field is located is dynamically determined, thereby driving the differentiated execution path of the tag normalization processing strategy. When the semantic stability value of a job position is less than or equal to the second stability threshold, the field is identified as a low-confidence region, indicating that its semantic structure is not yet stable and the normalization fluctuation is large. At this time, the parsing structure of the field and its job task context are cached and archived together, and the recalculation frequency of the field in the normalization process is reduced, freeing up computing resources to support the label generation task of subsequent fields and improving the overall task scheduling efficiency. When the semantic stability value of a job position is between the second and first stability thresholds, it is identified as a medium-confidence region, indicating that the field normalization result has a certain degree of stability but still has a slight risk of deviation. At this time, the dynamic structure mapping mechanism is activated to adjust the sorting priority of candidate labels and retain the reversible adjustment permission of the normalized labels, giving it flexible optimization capabilities. When the semantic stability value of a job position exceeds the first stability threshold, it indicates that the field expression is stable and the label normalization result is highly reliable. The field no longer participates in the label negotiation and structure reconstruction process and enters the normalized label freezing state. At the same time, the horizontal comparison task between the label vector and the course target library is triggered, providing direct support for the configuration of downstream capability models and the adaptation of teaching tasks. This process establishes a tag-normalized dynamic and reliable scheduling mechanism based on the stable semantic values ​​of job positions, which significantly improves the accuracy, efficiency, and robustness of the field processing chain.

[0072] Specifically, the semantic consistency of the label trajectory of the normalized field with its historical attribution results is compared to output semantic drift risk items and generate optimization suggestions. The specific steps are as follows: During the normalization process, the mapping relationship between each normalized field and its matched standard label is continuously recorded, and a longitudinal tracking matrix is ​​constructed based on the historical normalization records. The stability performance of the normalized label is compared field by field across different survey batches. When significant differences are found in the normalization results of the same field in multiple batches, i.e., it is normalized to different labels multiple times, it is considered a potential semantic drift risk item and is immediately included in the dynamic tracking sequence for monitoring. At the same time, the normalization confidence fluctuation value and semantic parsing strength value corresponding to the field are retrieved to analyze whether there are high-frequency change characteristics in its normalization path, and to determine whether the candidate label list is frequently changed and whether the confidence level fluctuates drastically. In addition, the semantic stability of the attribution is cross-validated by combining the label aggregation pattern of the current job category. The semantic backbone is identified by the label clustering trend to further determine whether the field has a stable normalization attribution. Finally, a normalized quality level assessment label is generated and included in the job competency profile evolution log for archiving and updating, thus constructing a historical change link of the job competency structure. For concentrated sections with a large number of semantic drift risk items, core fields with highly inconsistent normalization results, and functional areas with significantly insufficient coverage density of capability tags, the system automatically outputs a list of normalization optimization suggestions, prompting relevant personnel to supplement targeted content and optimize tag structure. This helps identify blind spots in capability coverage and areas of ambiguity in the curriculum system, thereby improving the relevance of curriculum construction and the stability of the tag normalization process.

[0073] This implementation plan conducts longitudinal tracing and risk identification to ensure the stability and consistency of the normalization results. It combines dynamic monitoring of semantic drift risk items with confidence analysis of the normalization path to assess the reliability of field semantic attribution and determines the rationality of normalization based on the label distribution characteristics of job categories. Through normalization quality level assessment and change log generation, an evolutionary record of job competency profiles is constructed, providing a basis for subsequent label correction, course content supplementation, and competency coverage optimization. This improves the accuracy of normalized expression, the stability of job labels, and the completeness of course design.

[0074] The second aspect of this invention provides an intelligent survey and data analysis platform for vocational education employment needs, including: a data acquisition and preprocessing module. This module simultaneously deploys multi-source sensing terminals and a structured input interface during the enterprise survey process to collect job text data covering job descriptions, behavioral phrases, and task expression fields. It also collects contextual behavioral data in real time based on user operation paths and field usage frequency, and automatically extracts feature structure data such as the total length of expression fields, the distribution of modifiers, and the number of verb-object pairs using a lexical scanner and syntactic analysis tools. All raw data undergoes format validation, semantic normalization, and cross-industry terminology alignment before normalization, ultimately constructing a standardized job text corpus dataset with a unified structure and clear semantics.

[0075] The semantic parsing module for expression fields invokes semantic deconstruction algorithms on candidate fields and evaluates their semantic parsing strength, primarily considering multi-dimensional indicators such as the complexity of the field's expression structure, the density of capability features, and the semantic bias ratio. When the parsing strength value exceeds a set threshold, the parsing path is automatically switched, and the weight of behavior-guided word recognition and the label matching window are adjusted to enhance the parsing channel's adaptability to complex expressions.

[0076] The expression field normalization mapping module further combines the semantic distribution features of candidate labels to extract the semantic clustering deviation mean square value and the multi-source correction value of the normalized attribute of the labels involved in the normalization process, and calculates the current normalization confidence fluctuation value. When the confidence fluctuation value is higher than the stable interval threshold, the context reconstruction process is immediately triggered to dynamically generate a new label candidate sequence and reset the field semantic mapping entry, thereby improving the adaptability and robustness of the normalization path.

[0077] The job competency tag construction module uses semantic parsing strength and confidence fluctuation as dual input benchmarks, integrates semantic concentration coefficient, tag support and stability correction value, comprehensively evaluates the job credibility support of each field for the tag results, and drives the precision control mechanism in the tag selection process accordingly, giving priority to retaining tag sets with high semantic consistency, short tag span and stable call frequency.

[0078] The competency semantic feedback and verification module records the mapping trajectory between each normalized field and the historical tag attribution record, performs tag semantic vector alignment analysis, detects whether there are tag drift, attribution changes or semantic ambiguity trends, and forms a list of semantic drift risk items; and generates structured optimization suggestions by combining the job competency structure distribution, tag call stability and historical normalization frequency to identify course coverage blind spots and tag correction priorities.

[0079] In this implementation plan, the data acquisition and preprocessing module comprehensively collects multi-dimensional semantic information during the job survey process, including job text data, contextual behavior data, and feature structure data. After collection, all data undergoes standardization and normalization processing to correct differences in industry terminology, unify semantic expression structures, and construct a standardized job text corpus dataset for subsequent analysis.

[0080] The semantic parsing module for expression fields evaluates the semantic parsing strength of fields in the standardized job text corpus dataset. It quantifies the parsing difficulty of fields in terms of structural complexity, ability information density, and semantic bias, and dynamically adjusts the semantic parsing path of fields based on the strength value. It also optimizes the behavior-oriented word extraction strategy and matching window settings to improve the parsing adaptability to complex expression structures.

[0081] The expression field normalization mapping module evaluates the stability of normalization matching between fields and candidate labels, extracts semantic aggregation deviation, label normalization coverage and confidence fluctuation values ​​during the normalization process, and triggers the context reconstruction process when the confidence fluctuation is significant, dynamically adjusting the label candidate set and mapping entry, thereby improving the robustness and semantic consistency of the field normalization path.

[0082] The job competency tag construction module comprehensively evaluates the credibility of fields in the construction of job tags by considering the assessment results of semantic parsing strength and normalized confidence fluctuation. Based on the credibility level, it adjusts the tag sorting, filtering and weighting mechanism to accurately control the quality of the final tag output results and enhance the structural integrity and semantic accuracy of job competency expression.

[0083] The Capability Semantic Feedback and Verification Module records the historical mapping path between the normalized field and the label, analyzes the consistency and stability of label attribution, identifies label drift risk and normalization fluctuation areas, and outputs semantic optimization suggestions to help identify capability coverage blind spots in the curriculum design, providing key basis for subsequent label reconstruction and alignment with curriculum objectives.

[0084] 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.

[0085] 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. Clearly, 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 method for intelligent survey and data analysis of employment demand in vocational education, characterized by: include: S1. Collect job text data, contextual behavior data, and feature structure data during the enterprise survey process, and preprocess the collected job text data, contextual behavior data, and feature structure data to construct a standardized job text corpus dataset. S2, based on a standardized job text corpus dataset, evaluates the semantic parsing strength of fuzzy expression fields and dynamically adjusts the semantic parsing path selection based on the evaluation results; S3, based on a standardized job text corpus dataset, analyzes the degree of confidence fluctuation of each field mapping by combining candidate label features, and dynamically drives context reconstruction based on the analysis results; The specific steps for analyzing the degree of confidence fluctuation of each field mapping based on the standardized job text corpus dataset and candidate label features are as follows: Extract the semantic similarity of all candidate labels in the normalization process for the current field, calculate the difference between the semantic similarity of the current candidate label and the average semantic similarity of all candidate labels, divide it by the average semantic similarity, and then square it to obtain the candidate semantic clustering deviation. Sum the candidate semantic clustering deviations of all candidate tags and divide by the number of candidate tag sets corresponding to the current field to obtain the mean square value of semantic clustering deviation; The number of times each label in the current field is shared by multiple fields is recorded as the label normalization coverage. The label normalization path of all fields under the job position is traversed, the position difference of the field corresponding to each label is calculated, and then the average is taken to obtain the average label span. The normalization coverage is divided by the average label span, and then multiplied by the ratio between the standard deviation of confidence and the maximum standard deviation of confidence in the field normalization mapping process plus one to obtain the multi-source correction value of the normalized attribute. Multiply the normalized attribute multi-source correction value by the corresponding normalized mapping adjustment factor and then add the semantic clustering deviation mean square value to obtain the normalized confidence fluctuation value of the current field. S4 uses the semantic parsing strength evaluation results and the confidence fluctuation analysis results as inputs to comprehensively evaluate the job credibility support of the tag results, and drives the tag selection accuracy control based on the evaluation results; S5 performs a semantic consistency comparison between the label trajectory of the normalized field and the historical attribution results, outputs semantic drift risk items, and generates optimization suggestions.

2. The intelligent survey and data analysis method for vocational education employment demand according to claim 1, characterized in that: The specific steps for collecting job text data, contextual behavior data, and feature structure data during the enterprise survey process, and preprocessing the collected job text data, contextual behavior data, and feature structure data to construct a standardized job text corpus dataset are as follows: Collect job text data generated during the enterprise survey process. The job text data includes: the number of ability keywords, semantic concentration coefficient, and normalized tag call frequency. At the same time, the total number of normalized tags in the current job description, the total frequency of normalized tag calls, and the number of candidate tag sets corresponding to each field are statistically recorded. Collect contextual behavioral data related to ambiguity, including: the subject-predicate structure type of the statement in which the field is located and semantic similarity; calculate and record the average semantic bias value, average semantic similarity, and the number of times each field is normalized to different labels in multi-job surveys for all fields under the same job category. The feature structure data collected during the survey process includes: the total length of each expression field, the standard deviation of confidence in the field normalization mapping process, and the maximum standard deviation of confidence in all fields; The collected job text data, context behavior data, and feature structure data are standardized and normalized to correct semantic ambiguities caused by differences in industry terminology. The standardized and normalized job text data, context behavior data, and feature structure data are then archived and stored in a unified manner to construct a standardized job text corpus dataset.

3. The intelligent survey and data analysis method for vocational education employment demand according to claim 1, characterized in that: The specific steps for evaluating the semantic parsing strength of fuzzy expression fields based on the standardized job text corpus dataset are as follows: The LTP dependency parsing tool was used to extract the number of verb-object pairs from the expression fields in the standardized job text corpus dataset. At the same time, modifiers were extracted and the distribution deviation of the positions of modifiers in the fields was calculated to obtain the standard deviation of modifiers. Extract the total length of the expression fields from the standardized job text corpus dataset, multiply the total length of the expression fields by the standard deviation of modifiers, and then divide by the value after adding one to the number of verb-object pairs in the expression fields to obtain the basic structure complexity; The number of capability keywords appearing in the statistical field is divided by the total length of the field to obtain the capability feature density. The square of the capability feature density is calculated, one is added, the logarithm is taken, and then it is added to the basic structure complexity to obtain the preliminary semantic deconstruction degree. By combining word embedding model with domain corpus classification model, the similarity distribution of fields in the label semantic space is calculated to obtain the context semantic bias value of each field to which the job belongs. At the same time, the average semantic bias value of all fields under the same job category is calculated. The absolute value of the difference between the context semantic bias value and the average semantic bias value is calculated, divided by the average semantic bias value and then added by one to obtain the attribution semantic bias ratio. Extract the number of disagreements in the current field that are normalized to different labels in the multi-job survey, and divide the number of disagreements by the total number of normalized labels in the current job description to obtain the high frequency disagreement rate. The semantic parsing strength value is obtained by multiplying the initial semantic deconstruction degree by the ratio of attribution semantic deviation and then subtracting the high-frequency divergence rate.

4. The intelligent survey and data analysis method for vocational education employment demand according to claim 1, characterized in that: The specific steps for dynamically adjusting the semantic parsing path selection based on the evaluation results are as follows: Real-time comparison of the semantic parsing strength value of the current fuzzy expression field with the semantic parsing recognition threshold: When the semantic parsing strength value is less than or equal to the semantic parsing recognition threshold, it is determined to be a segment with clear semantic structure. The original semantic parsing path and the unified label allocation strategy remain unchanged, and the field standardization process and capability label mapping mechanism continue to run. There is no need to perform parsing rule reconstruction and intervention labeling operations, and the normal operation status of the unified module processing channel is maintained. When the semantic parsing strength value is greater than the semantic parsing recognition threshold, it is determined to be a segment with complex semantic structure and enters the high-complexity expression parsing process: immediately trigger the context reconstruction mechanism, prioritize extracting the behavioral guide words of the preceding and following sentences in the segment where the field is located, simultaneously start the multi-label matching channel, shorten the backtracking window length of the field parsing channel, and improve the drift detection frequency.

5. The intelligent survey and data analysis method for vocational education employment demand according to claim 1, characterized in that: The specific steps for dynamically driving context reconstruction based on the analysis results are as follows: When the normalized confidence fluctuation value shows an increasing trend in three consecutive fields and the increase gradually expands, it is determined that the current field is in a high uncertainty accumulation range. The context reconstruction mechanism is immediately activated, the paragraph to which the field belongs is expanded one or two sentences before and after, the combination of behavior-oriented words and ability-modifying words is re-analyzed, and the original expression and context combination are uniformly processed to construct a semantically enhanced label structure. The field is then recorded as an expression to be stabilized. When the normalized confidence fluctuation value fluctuates frequently between multiple fields, and the increase in the difference between the upper and lower values ​​is significant, it is determined to be a semantic transition interval. The field is labeled with both primary and secondary labels, and a one-to-many label reference table is established to retain all candidate labels and their semantic relationships. When the normalized confidence fluctuation value has a very small fluctuation range and a concentrated value range in multiple consecutive fields, it is determined that the current normalization state is in the semantic convergence interval. Then, the candidate label with the highest similarity is selected as the normalization label of the field, and the mapping path is marked as unidirectionally stable and included in the standard normalization result library.

6. The intelligent survey and data analysis method for vocational education employment demand according to claim 1, characterized in that: The specific steps for comprehensively evaluating the job credibility support of the tag results, using semantic parsing strength assessment results and confidence fluctuation analysis results as input, are as follows: Obtain the semantic parsing strength value and the normalized confidence fluctuation value. Divide the semantic parsing strength value by the value after adding one to the semantic parsing strength value, and then multiply it by the value after adding one to the semantic concentration coefficient and taking the logarithm to obtain the concentration complexity. The frequency of normalized labels in the job field is statistically analyzed and the standard deviation is calculated. Then, it is normalized to the job label smoothness. The current normalized label call frequency is divided by the sum of the total normalized label call frequency and the job label smoothness to obtain the label support. Divide one by the value of the normalized confidence fluctuation value plus one, and then add one to the value to obtain the stability correction value; Multiply the central complexity, label support, and stability correction value to obtain the job semantic stability value.

7. The intelligent survey and data analysis method for vocational education employment demand according to claim 1, characterized in that: The specific steps for controlling the label selection accuracy based on the evaluation results are as follows: Real-time comparison of job semantic stability values ​​with tags to construct stability grading thresholds, which include a first stability threshold and a second stability threshold: When the semantic stability value of a job is less than or equal to the second stability threshold, it is determined to be a low-confidence segment. The semantic parsing structure of the field and the job task context are cached and archived to reduce the frequency of normalization recalculation and release computing resources to support subsequent field tag generation tasks. When the semantic stability value of a job is greater than the second stability threshold and less than or equal to the first stability threshold, it is determined to be a medium-credibility segment, dynamic structure mapping is enabled, the candidate label sorting strategy is adjusted, and reversible adjustment permissions are retained. When the semantic stability value of a job exceeds the first stability threshold, it is determined to be a high-confidence segment. Fields in the current window no longer participate in the label negotiation and structure reconstruction tasks and enter a frozen state. At the same time, the horizontal comparison process between the label vector and the course target library is triggered.

8. The intelligent survey and data analysis method for vocational education employment demand according to claim 1, characterized in that: The specific steps for performing semantic consistency comparison between the label trajectory of the normalized field and the historical attribution results, outputting semantic drift risk items, and generating optimization suggestions are as follows: Record the correspondence between each normalized field and the standard label, perform vertical comparison of each normalization result, and mark the same field as a semantic drift risk item when it is normalized to multiple labels in different batches and include it in the tracking sequence. At the same time, call the calculation results of normalization confidence fluctuation value and semantic parsing strength value to evaluate whether there are high-frequency changes in the label normalization path and whether the candidate labels are frequently replaced. Combine the label clustering trend of job category to determine whether the field has a stable semantic attribution, output the normalization quality level, and generate the change log of job capability profile. Optimization suggestions are provided for areas with concentrated semantic drift risk items, fields with frequent normalization changes, and regions with uneven capability distribution, to help personnel identify blind spots in course coverage.

9. The intelligent survey and data analysis platform for vocational education employment demand as described in any one of claims 1-8, characterized in that: include: The data acquisition and preprocessing module is used to collect job text data, contextual behavior data, and feature structure data during the enterprise survey process, and to preprocess the collected job text data, contextual behavior data, and feature structure data to construct a standardized job text corpus dataset. The semantic parsing module for expression fields is used to evaluate the semantic parsing strength of fuzzy expression fields based on a standardized job text corpus dataset, and dynamically adjust the semantic parsing path selection based on the evaluation results; The expression field normalization mapping module is used to analyze the degree of confidence fluctuation of each field mapping based on a standardized job text corpus dataset and candidate label features, and dynamically drive context reconstruction based on the analysis results. The job competency tag construction module is used to comprehensively evaluate the job credibility support of the tag results by taking the semantic parsing strength evaluation results and the confidence fluctuation degree analysis results as inputs, and drive the tag selection accuracy control based on the evaluation results; The Capability Semantic Feedback and Verification Module is used to compare the semantic consistency of the label trajectory of the normalized field with the historical attribution results, output semantic drift risk items and generate optimization suggestions.

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