A method and system for constructing a multi-dimensional portrait of professional competence for career guidance

Abstract

The present application relates to the technical field of user portrait construction, in particular to a multi-dimensional portrait construction method and system for career ability of employment guidance, comprising the following steps: extracting project source code features, comparing open source templates to generate coincidence analysis records, adjusting basic performance output assessment indicators according to coincidence proportion, analyzing word frequency sequence fitting to obtain attenuation slope and output evolution records, comparing elimination boundaries to calculate loss products and generate compensation records, and constructing multi-dimensional portraits according to log comparison, control node mapping and alternative features, in the present application, a punishment constraint mechanism is established by detecting code coincidence degree to accurately strip test brushing water, time sequence fitting is introduced to mine skill attenuation trajectory, and dynamic loss calculation is combined to correct time limit evaluation deviation, quantitative invisible business practice is converted into bottom quality compensation indicators, and an objective multi-dimensional evaluation coordinate system is established to make personnel evaluation dimension closely integrated with real industry dynamic labor limit.

Description

Technical Field

[0001] This invention relates to the field of user profile construction technology, and in particular to a method and system for constructing a multi-dimensional profile of professional competence for employment guidance. Background Technology

[0002] User profiling technology encompasses a complete process system for extracting target subject features from multi-source heterogeneous data and transforming them into structured tags. This mainly involves core aspects such as multi-channel data collection, data cleaning and deduplication, feature entity recognition, tag system tree planning, and behavioral trajectory and basic attribute correlation mining. It establishes a digital abstract model describing the target subject by integrating static demographic attributes and dynamic records of interactive behavior. Traditional methods for constructing multi-dimensional profiles of career abilities used in employment guidance refer to the process of quantitatively characterizing job seekers' professional skills and career suitability. This typically relies on manually distributed questionnaires to collect academic transcripts and internship experience texts. Instructors then use pre-set career aptitude test scales to score and summarize scale features, employing fixed threshold comparisons or linear weighting formulas to calculate specific scores for each skill dimension, generating a basic ability radar chart. Finally, the scores for each dimension are directly mapped at the character level and compared with the hard indicators for positions published by companies, such as educational level, foreign language proficiency, and qualification certificate categories.

[0003] Traditional vocational competency assessments rely on manually distributing questionnaires to collect scores and combining them with fixed scales for scoring. The scores are calculated using a linear weighting formula and then compared with the company's hard indicators at the character level. This static collection and surface information verification operation mode makes it difficult to penetrate the underlying logic of job seekers' true coding abilities. It is also highly susceptible to interference from test templates, leading to distorted technical assessments. At the same time, it ignores the dynamic decay of recruitment needs over time, causing the personnel assessment boundary to lag behind the actual employment benchmarks in the industry. Furthermore, it is difficult to objectively quantify the incremental business skills brought about by the transformation of implicit internship experience. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by proposing a method and system for constructing a multi-dimensional profile of vocational abilities for employment guidance.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a method for constructing a multi-dimensional profile of vocational abilities for employment guidance, comprising the following steps: S1: Extract the character sequence of the source code of the user's training project and the character sequence of the open source template in the open source resource library, compare the overlap status, calculate the total intersection and code overlap ratio parameters, and generate source code overlap parsing records; S2: Analyze the source code overlap parsing record, analyze the project source code to extract custom variables and exception handling nodes to calculate the total parameters, combine the basic theoretical scores to calculate the product, use the code overlap ratio parameter to adjust the product to obtain the assessment indicators, and generate the test reshaping assessment record; S3: Analyze historical recruitment texts to obtain the target skill word frequency parameter, calculate and minimize the sum of squared errors in the target skill word frequency time series, optimize the minimization of the sum of squared errors and perform linear regression fitting, calculate the skill demand decay slope, and output the skill demand evolution record. S4: Obtain the industry elimination threshold, compare it with the skill requirement decay slope parameter in the skill requirement evolution record, obtain the skill assessment record, deduct the product of the loss ratio to generate the overflow parameter, and sum it with the basic ability rating to generate the underlying ability compensation record. S5: Extract non-standard business instructions from the user's internship work log, extract standard control nodes by combining them with job requirement standards and compare them, calculate the product of feature parameters and conversion ratio to obtain alternative indicators, and construct a multi-dimensional profile of the user's professional ability by combining the test reshaping assessment record and the underlying ability compensation record.

[0006] As a further aspect of the present invention, the source code overlap parsing record includes an original logic fragment table, template dependency tags, and a similar file index list; the exam reshaping assessment record includes a practical net value score, an engineering standardization index, and a theoretical water content stripping sheet; the skill demand evolution record includes a technology life cycle prediction table, a market popularity downgrade list, and a framework obsolescence warning card; the underlying capability compensation record includes a skill transfer conversion detail, a basic potential assessment card, and a core competency minimum score; and the user's multi-dimensional professional capability profile includes an employment competitiveness radar chart, a job suitability dashboard, and a weakness advancement roadmap.

[0007] As a further aspect of the present invention, the process for obtaining the source code overlap parsing record is as follows: S111: Obtain the authorized public sample dataset, retrieve the local training project data frame, scan the text content in the training project data frame to extract the source code character sequence, connect to the open source resource library to extract the open source template character sequence, perform format standardization conversion and deduplication filtering on the source code character sequence and the open source template character sequence, map the text feature vector dimension according to the text content length, and generate a sequence alignment matrix; S112: Call the sequence alignment matrix to extract the character sequence of the project source code and the character sequence of the open source template, monitor the overlapping state attribute of the character sequence of the project source code and the character sequence of the open source template in the vector space, start the character traversal and comparison instruction to determine the matching text node, extract the position coordinates of the matching text node, accumulate the count value of the matching text node according to the increment rule, and generate the total number of intersection elements. S113: Call the total number of intersection elements, detect the length allocation status value for the open source template character sequence, extract the ratio calculation benchmark value, perform scalar division operation to process the total number of intersection elements and the length allocation status value, output the code overlap ratio parameter, aggregate the total number of intersection elements and the code overlap ratio parameter to configure associated key-value pairs, map the associated data topology, and establish source code overlap parsing records.

[0008] As a further aspect of the present invention, the process for obtaining the examination reshaping assessment record is specifically as follows: S211: Analyze the source code overlap parsing record, extract the code overlap ratio parameter, retrieve the project source code character sequence and read the custom variable statement and exception handling node respectively, perform a counting operation on the custom variable statement to obtain the statement quantity, count the exception handling node to obtain the node quantity, perform arithmetic summation on the statement quantity and the node quantity to generate the summation parameter; S212: Call the basic theoretical score from the database, multiply the basic theoretical score by the summation parameter, and generate the product parameter; S213: Collect the number of lines in the life domain of custom variable statements, detect the number of skipped lines in exception handling nodes, obtain the baseline number of lines, obtain the total number of matches, calculate and obtain the assessment indicators, and establish the test reshaping assessment record.

[0009] As a further aspect of the present invention, the process for obtaining the skill requirement evolution record is as follows: S311: Analyze the company's historical recruitment texts, extract multiple categories of professional skill characters, perform word segmentation comparison and traversal counting on the extracted professional skill characters in the text sequence after time series segmentation, accumulate the occurrence frequency of each character, and generate target skill word frequency parameters; S312: Call the target skill word frequency parameter, obtain the sampling time node and matching base, calculate the error sum of squares minimization state, extract the smoothing period and confidence factor to optimize the error sum of squares minimization state and perform linear regression fitting, collect the total span, and calculate the decay slope parameter; S313: Call the attenuation slope parameter to analyze the evolution trend of the target skill word frequency parameter, determine the changes in enterprise demand for various professional skills, and establish a skill demand evolution record.

[0010] As a further aspect of the present invention, the process for obtaining the underlying capability compensation record is as follows: S411: Obtain the industry elimination threshold, retrieve the skill requirement evolution record, extract the skill requirement decay slope parameter, compare the industry elimination threshold with the skill requirement decay slope parameter, determine the over-limit state, and extract matching feature values ​​based on the over-limit state attributes to generate a skill assessment record. S412: Call the skill assessment record, extract the preset loss ratio coefficient, retrieve the quantitative value inside the skill assessment record, multiply the quantitative value and the preset loss ratio coefficient, output the assessment loss product term, adjust the skill assessment record to deduct the product term, and obtain the overflow parameter; S413: Based on the overflow parameter, obtain the capability rating parameter table, extract the user's basic capability rating value, perform an addition operation between the overflow parameter and the basic capability rating value, output the compensation sum parameter, combine the personnel identity identifier, configure the association mapping attribute between the compensation sum parameter and the personnel identity identifier, and establish the underlying capability compensation record.

[0011] As a further aspect of the present invention, the process of obtaining the industry elimination threshold specifically includes: Obtain the historical word frequency evolution data sequence of obsolete occupational skills from authorized public statistical data, extract the tail time slices of the historical word frequency evolution data sequence in descending order, perform linear regression analysis on the tail time slices to obtain the historical delisting slope set, calculate the set mean and standard deviation of the historical delisting slope set, and output the lower limit boundary value as the industry elimination limit by subtracting the standard deviation from the set mean. The process of extracting the preset loss ratio coefficient is as follows: Perform an algebraic subtraction operation on the skill demand attenuation slope parameter and the industry elimination limit to output the excess deviation difference. Obtain the absolute value of the excess deviation difference. Divide the absolute value by the absolute value of the industry elimination limit to generate a relative decay ratio parameter. Perform an exponential function operation with the natural constant as the base and the negative number of the relative decay ratio parameter as the exponent to extract the decay factor. Subtract the decay factor from 1 to output the preset depreciation ratio coefficient.

[0012] As a further aspect of the present invention, the process for obtaining the multi-dimensional profile of a user's professional abilities is as follows: S511: Analyze the internship work logs of target users, extract non-standard business instructions, analyze the standard documents of job requirements, extract standard control nodes, perform set intersection operations on non-standard business instructions and standard control nodes, filter and match character feature items, aggregate overlapping text fragments to configure logical mapping key-value pairs, and establish a common node set; S512: Call the shared node set, count the frequency attributes of text-level nodes and calculate the node feature parameters by combining them with the weight coefficients, obtain the preset conversion ratio, calculate the product of the feature parameters and the conversion ratio, and generate alternative indicators. S513: Call the alternative indicators, read the assessment indicators of the test reshaping assessment record, retrieve the summation parameters from the underlying ability compensation record, perform structural standardization alignment on the alternative indicators, assessment indicators and summation parameters, combine vector concatenation to output a three-dimensional feature vector, configure the multi-dimensional feature space coordinate mapping relationship, construct a global data topology structure, and establish a multi-dimensional profile of the user's professional ability.

[0013] A system for constructing a multi-dimensional profile of vocational abilities for employment guidance, wherein the system executes the aforementioned method for constructing a multi-dimensional profile of vocational abilities for employment guidance, and the system includes: The project comparison and analysis module extracts the character sequence of the source code of the user's training project and the character sequence of the open source template in the open source resource library, compares the overlap status, calculates the total intersection and code overlap ratio parameters, and generates source code overlap analysis records. The assessment result analysis module analyzes the source code overlap parsing record, analyzes the project source code to extract custom variables and calculate the total parameters of exception handling nodes, calculates the product with the basic theory score, adjusts the product using the code overlap ratio parameter to obtain the assessment index, and generates the test reshaping assessment record. The Skills Evolution Analysis module analyzes historical recruitment texts to obtain target skill word frequency parameters, calculates and minimizes the sum of squared errors in the target skill word frequency time series, optimizes the minimization of the sum of squared errors and performs linear regression fitting, calculates the skill demand decay slope, and outputs a record of skill demand evolution. The capability compensation calculation module obtains the industry elimination threshold, compares it with the skill requirement decay slope parameter of the skill requirement evolution record, obtains the skill assessment record, deducts the product of the loss ratio to generate an overflow parameter, and sums it with the basic capability rating to generate the underlying capability compensation record. The user profile building module extracts non-standard business instructions from the user's internship work log, extracts and compares standard control nodes with job requirement standards, calculates the product of feature parameters and conversion ratio to obtain alternative indicators, and constructs a multi-dimensional profile of the user's professional ability by combining the test reshaping assessment record and the underlying ability compensation record.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, source code sequences are extracted and compared with open-source templates to calculate the total intersection and overlap ratio to generate parsing records. Variables and abnormal nodes are deeply mined and summed, and cross-validated with basic scores. Attenuation penalties are implemented based on code overlap parameters to construct assessment indicators that represent real engineering development capabilities, thereby accurately eliminating test-taking bias. Temporal error is introduced to fit and capture the attenuation slope of historical recruitment word frequencies. Dynamic depreciation calculations are performed by comparing with industry boundaries to compensate for the lag error in skill timeliness assessment. Business instructions are extracted from non-standard work logs and compared with standard control nodes to map replacement features. Implicit practical experience is quantified and transformed into underlying capability compensation items, constructing a global graph integrating multiple dimensions. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the workflow of the present invention; Figure 2 This is a flowchart of the source code overlap parsing and record acquisition process of the present invention; Figure 3 This is a flowchart of the test reshaping assessment record acquisition process of the present invention; Figure 4 This is a flowchart for obtaining skill requirement evolution records in this invention; Figure 5 This is a flowchart of the underlying capability compensation record acquisition process of the present invention; Figure 6 This is a flowchart illustrating the process of obtaining a multi-dimensional profile of a user's professional abilities according to the present invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0017] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0018] Please see Figure 1 This invention provides a technical solution: a method for constructing a multi-dimensional profile of vocational abilities for employment guidance, comprising the following steps: S1: Extract the character sequence of the source code of the user's training project and the character sequence of the open source template in the open source resource library, compare the overlap status, calculate the total intersection and code overlap ratio parameters, and generate source code overlap parsing records; S2: Analyze the source code overlap parsing records, analyze the project source code to extract custom variables and exception handling nodes to calculate the total parameters, combine the basic theoretical scores to calculate the product, use the code overlap ratio parameters to adjust the product to obtain the assessment indicators, and generate the test reshaping assessment record; S3: Analyze historical recruitment texts to obtain the target skill word frequency parameter, calculate and minimize the sum of squared errors in the target skill word frequency time series, optimize the minimization of the sum of squared errors and perform linear regression fitting, calculate the skill demand decay slope, and output the skill demand evolution record. S4: Obtain the industry elimination threshold, compare it with the skill demand decay slope parameter in the skill demand evolution record, obtain the skill assessment record, deduct the product of the loss ratio to generate the overflow parameter, and sum it with the basic ability rating to generate the underlying ability compensation record. S5: Extract non-standard business instructions from user internship work logs, extract standard control nodes by combining them with job requirement standards and compare them, calculate the product of feature parameters and conversion ratio to obtain alternative indicators, and construct a multi-dimensional profile of user professional ability by combining test-oriented reshaping assessment records and underlying ability compensation records.

[0019] The source code overlap analysis record includes an original logic fragment table, template dependency tags, and a similar file index list; the exam reshaping assessment record includes practical net value score, engineering standardization index, and theoretical water content stripping sheet; the skill demand evolution record includes a technology life cycle prediction table, a market popularity downgrade list, and a framework obsolescence warning card; the underlying ability compensation record includes skill transfer conversion details, basic potential assessment card, and core competency minimum score; and the user career ability multi-dimensional profile includes an employment competitiveness radar chart, a job suitability dashboard, and a weakness advancement roadmap.

[0020] Please see Figure 2 The specific process for obtaining the source code overlap parsing record is as follows: S111: Obtain the authorized public sample dataset, retrieve the local training project data frame, scan the text content in the training project data frame to extract the source code character sequence, connect to the open source resource library to extract the open source template character sequence, perform format standardization conversion and deduplication filtering on the source code character sequence and the open source template character sequence, map the text feature vector dimension according to the text content length, and generate a sequence alignment matrix; A network communication connection is established with a public open-source code hosting platform through an authorized application programming interface (API). A Hypertext Transfer Protocol (HTTP) request with timestamps and resource identifiers is sent to obtain an authorized public sample dataset containing 50,000 open-source projects. This dataset covers various categories of text resources, including front-end rendering, back-end logic, and database interaction. Training project data frames are retrieved from local storage. Text content with extensions .java and .py is read, scanned, and segmented according to newline characters and semicolons. The resulting sequence of project source code characters, consisting of consecutive English letters, numbers, and operators, is extracted. The open-source repository is connected, and the corresponding language-architecture open-source template character sequence is extracted from its full data copy. For the extracted project source code character sequence and open-source template character sequence, a format standardization conversion is performed. Specific steps include: converting all uppercase letters to lowercase; removing comment lines starting with specific introductory characters such as double slashes or hash symbols; deleting all tabs and redundant spaces; and retaining only core syntax identifiers and business logic characters. During the deduplication filtering operation, a hash set is established to filter discrete variable names shorter than 5 characters to reduce the interference of basic syntactic elements on similarity calculation. Based on the total number of valid characters in the filtered text, every 10 consecutive valid characters are considered as a word block. The total number of all unique word blocks is counted and directly mapped to the dimension of the text feature vector. A two-dimensional matrix with all initial elements set to 0 is constructed, where the number of rows corresponds to the number of word blocks in the training project and the number of columns corresponds to the number of word blocks in the open-source template. The two character sequences are traversed. If the word block content at a specific position is completely identical between the feature space of the training project and the feature space of the open-source template, the value at the corresponding row and column intersection of the matrix is ​​updated to 1. This generates a sequence alignment matrix for subsequent precise comparison.

[0021] S112: Call the sequence alignment matrix to extract the character sequence of the project source code and the character sequence of the open source template, monitor the overlapping state attribute of the character sequence of the project source code and the character sequence of the open source template in the vector space, start the character traversal and comparison instruction to determine the matching text node, extract the position coordinates of the matching text node, accumulate the count value of the matching text node according to the increment rule, and generate the total number of intersection elements. The sequence alignment matrix instantiated in memory is invoked to extract the character sequences of the project source code represented by its row vectors and the character sequences of the open-source templates represented by its column vectors. The overlap state attributes of these character sequences in the multi-dimensional feature vector space are monitored. A character traversal and comparison instruction is initiated, starting from the first row and first column of the sequence alignment matrix, scanning the matrix elements row by row and column by column, determining whether the value at the current coordinate position is 1. When a value of 1 is detected, it is identified as a matching text node, and the row and column numbers of this matrix element are extracted as the position coordinates of the matching text node and written to an associative array. An integer variable is initialized as a counter value, initially set to 0. Following an incrementing rule, for each matching text node identified in the sequence alignment matrix, the current value of this integer variable is incremented by 1. This accumulation operation continues until the last row and last column of the sequence alignment matrix are traversed, and the final integer variable value is used as the total number of intersection elements. In a training project involving a backend user management module, the generated sequence alignment matrix was 1500 rows by 8000 columns. After a full matrix scan, 450 position coordinate records were extracted. After accumulation according to an increasing rule, the total number of intersection elements was 450. This accumulation process transforms the sparse matrix characteristics in high-dimensional space into one-dimensional scalar values, intuitively reflecting the absolute scale of local code borrowing from open-source templates.

[0022] S113: Call the total number of intersection elements, detect the length allocation status value for the open source template character sequence, extract the ratio calculation baseline value, perform scalar division operation to process the total number of intersection elements and the length allocation status value, output the code overlap ratio parameter, aggregate the total number of intersection elements and the code overlap ratio parameter to configure associated key-value pairs, map the associated data topology, and establish source code overlap parsing records; The total number of intersection elements generated by the above accumulation is used. For the open-source template character sequence, the total number of valid word blocks occupied in memory is read as the detection length allocation status value. A baseline value for the ratio calculation is extracted and set to 1 to ensure the standard percentage range constraint of the calculation result. A scalar division operation is performed on the total number of intersection elements and the length allocation status value. The total number of intersection elements is used as the dividend, and the length allocation status value is used as the divisor to calculate a small value between 0 and 1. This small value is output as the code overlap ratio parameter. The total number of intersection elements and the code overlap ratio parameter are aggregated, and each is used as a value. Corresponding key-value pairs are generated, for example, a key-value pair with an absolute overlap value of 450 and a relative overlap value of 0.35 is generated. The associated data topology is mapped. In the graph database, a unidirectional connection edge is established with the user's unique identifier as the central node and the generated key-value pairs as leaf nodes, creating a source code overlap parsing record. Taking a specific training project as an example, if the measured length distribution state value is 1285, the total number of intersection elements is 450, and the code overlap ratio parameter output after performing scalar division operation is 0.3501.

[0023] Table 1. Source Code Overlap Parameter Comparison Experiment Test Table As shown in Table 1, by performing scalar division operations and tracking records on multiple sets of practical data, the intersection calculation results of code files of different sizes and the corresponding extremely low judgment error rate are presented.

[0024] Please see Figure 3 The specific process for obtaining the examination reshaping assessment record is as follows: S211: Analyze the source code overlap parsing records, extract the code overlap ratio parameter, call the project source code character sequence and read the custom variable statements and exception handling nodes respectively, perform a counting operation on the custom variable statements to obtain the statement quantity, count the exception handling nodes to obtain the node quantity, perform arithmetic summation on the statement quantity and the node quantity to generate the summation parameter; The code overlap parsing records in the graph database are analyzed. Indexed by the user's unique identifier, the corresponding key-value pairs are extracted, and the code overlap ratio parameter is read. The character sequence of the project source code stored on the local hard drive is retrieved to construct an abstract syntax tree structure. A depth-first traversal is performed starting from the root node of the syntax tree, reading nodes of type variable declaration statements as user-defined variable statements, and nodes of type exception catching and handling as exception handling nodes. A counting operation is performed on the selected user-defined variable statements; the count is incremented by 1 for each independent, non-global reference to a local variable declaration, resulting in the statement count. A counting operation is also performed on exception handling nodes; the count is incremented by 1 for each complete block of code that catches and handles an exception, resulting in the node count. The obtained statement count and node count are then arithmetically summed, that is, the statement count representing the frequency of variable lifecycle management is directly added to the node count representing the robustness of the program design, generating a summation parameter. For example, when performing an abstract syntax tree traversal on the code of a user's e-commerce shopping cart module, the number of statements for locally defined custom variables such as product quantity and unit price is 45, the number of nodes for handling exceptions such as database connection timeout and null pointer is 12, and the total number of parameters generated after performing arithmetic addition is 57.

[0025] S212: Call the basic theoretical score from the database, multiply the basic theoretical score by the summation parameter, and generate the product parameter; The system uses a structured query language to retrieve basic theoretical scores recorded in the enterprise's internal academic affairs relational database. These scores include percentage-based raw scores from three core courses: Data Structures, Computer Networks, and Operating Systems, which have been preprocessed by averaging to extract floating-point numbers between 0 and 100. The extracted basic theoretical scores are then multiplied by a summation parameter generated by traversing the aforementioned abstract syntax tree. Specifically, the system reads the floating-point format basic theoretical scores and the integer format summation parameter, loads them into the arithmetic logic unit, performs algebraic multiplication, and generates a product parameter. In a specific business scenario, a database query retrieves a target user's average basic theoretical score of 82.5. Combined with the summation parameter value of 57 obtained from previous steps, 82.5 is multiplied by 57, resulting in a product parameter value of 4702.5. This multiplication process deeply integrates the theoretical scores, representing static knowledge reserves, with the summation parameter, representing dynamic engineering detail processing capabilities, constructing a two-dimensional evaluation benchmark.

[0026] S213: Collect the number of lines in the life domain of custom variable statements, detect the number of skipped lines in exception handling nodes, obtain the baseline line count, and obtain the total number of matches, using the following formula: ; Calculate and obtain assessment indicators, and establish test-oriented re-assessment records; in, As a performance indicator, The product parameter is obtained through algebraic multiplication. The code overlap ratio parameter is obtained by calling the parsing record. The baseline row number is obtained through global extraction. To match the total number, it was obtained by iterating and counting. The accumulated index variable for the nodes is obtained through traversal operations. For index The number of lines in the lifetime is obtained by subtracting the line numbers from the line numbers. For index The number of skipped rows is obtained by detecting the offset of abnormal nodes; The process involves collecting the line counts of the lifetimes of custom variable statements from the abstract syntax tree (AST) of the project source code. This is done by identifying the line number where the variable is first declared and initialized, and the line number where the variable is last effectively referenced. The line count of the lifetime is obtained by subtracting the line number of the former from the latter. The process also includes detecting the line jumps in exception handling nodes, locating the line number of the trigger point where the exception was thrown, and the line number of the entry block that caught the exception and executed the remedial logic. The absolute value of the difference between the entry block line number and the trigger point line number is used as the exception node offset and recorded as the line jump count. A baseline line count is obtained, which is derived from global statistics based on the average total number of lines of code in open-source templates of similar projects across the industry. This baseline is used to eliminate absolute numerical differences caused by different project sizes; here, the baseline line count is set to 5000. Finally, the total number of matches is obtained, which is the cumulative count of valid function blocks in the AST that simultaneously contain variable declarations and exception handling. Substitute the obtained product parameter value of 4702.5, code overlap ratio parameter value of 0.35, baseline line count of 5000, and total number of matches of 15 into the formula for calculation. The known parameters are as follows: product parameter... Code overlap ratio parameter Baseline row number against The square root of the absolute value of the product of the number of rows in the survival domain and the number of rows in the jump domain of a function block is obtained by summing the results through traversal. Substitute the above parameters into the performance indicator calculation formula: ; ; ; ; The calculated assessment indicator value is 3169.485. This value and the associated user identifier are used to establish a test-oriented reshaping assessment record and store it in a non-relational database. The innovation and beneficial effect of this formula lies in the introduction of a non-linear square root and survival domain mapping mechanism, combined with dynamic decay penalty based on overlap, which can adaptively suppress test-oriented test scores with high overlap and accurately discover original logic fragments with deep code coupling processing capabilities.

[0027] Please see Figure 4 The specific process for obtaining skill requirement evolution records is as follows: S311: Analyze the company's historical recruitment texts, extract multiple categories of professional skill characters, perform word segmentation comparison and traversal counting on the extracted professional skill characters in the text sequence after time series segmentation, accumulate the occurrence frequency of each character, and generate target skill word frequency parameters; By deploying a targeted web crawler cluster on a distributed server, we analyzed historical job postings from companies, specifically accessing public web interfaces of major domestic internet recruitment platforms to crawl 50,000 job descriptions for front-end development and back-end architecture positions posted over the past 36 months. We extracted multi-category professional skill characters, constructing a seed dictionary containing 500 common industry technology frameworks and programming language names. Regular expressions were used to precisely match and extract the technology stack requirements paragraphs in the job postings. The extracted professional skill characters were then processed in the time-series segmented text sequence, performing word segmentation comparison and traversal counting. Specifically, the 36-month time span was divided into 36 time-series segmented text subsets, with each calendar month as a segmentation unit. A natural language processing word segmentation tool based on a Hidden Markov Model was used to segment the sentences in each text subset. The segmentation results were compared with the seed dictionary; if the segmentation result completely matched a specific professional skill character in the dictionary, an accumulation command was triggered. In each natural month's text subset, the occurrence counts of each character are accumulated to generate the target skill word frequency parameter, forming a time series array containing 36 time nodes, each node corresponding to a specific frequency value. For example, regarding the performance of skill characters, the occurrence count is 1200 times in the text slice of month 1 and 350 times in the text slice of month 36. These monthly statistical values ​​are arranged in order to generate the target skill word frequency parameter set.

[0028] S312: Call the target skill word frequency parameter, obtain the sampling time node and matching cardinality, calculate the minimum state of the sum of squared errors, extract the smoothing period and confidence factor to optimize the minimum state of the sum of squared errors and perform linear regression fitting, collect the total span, and use the formula: ; Calculate the attenuation slope parameter; in, The attenuation slope parameter, The confidence factor is obtained through preset parameters. The total number of spans is obtained through span statistics. The index variable for the time series is obtained through iteration. For the first Frequency parameters of target skills The matching cardinality is obtained by calculating the mean. For the first Each sampling time point is acquired through clock data collection. To smooth out the cycle, it is obtained by using a time window. The system calls the target skill word frequency parameter time series array generated from the previous records. It obtains sampling time nodes, starting with the earliest segmentation month as point 1, and increments monthly to form the sampling time node sequence, i.e., obtaining consecutive integers from 1 to 36. It obtains the matching cardinality by extracting the highest peak word frequency of the skill in all past natural months as a benchmark reference point, and determines this value in the array using an extreme value search algorithm. It calculates the minimum error sum of squares state by setting an initial linear line with a negative slope, calculating the squared difference between the target skill word frequency parameter at each sampling time node and the corresponding coordinate point value on the line, and adjusting the slope and intercept of the line using a gradient descent algorithm until the sum of the squared differences at all time nodes reaches its minimum value. It extracts the smoothing period, setting its specific value to 6, representing the six-month recruitment peak and off-peak season cycle. It extracts the confidence factor, which is obtained through empirical calibration of the fitting residuals of 50 similar techniques within historical decline cycles; in this embodiment, the preset value is 0.85, used to optimize the minimum error sum of squares state and perform linear regression fitting. It collects the total number of spans, i.e., the total number of time slices, 36.

[0029] Assuming matching cardinality Confidence factor Smoothing period Total span The differences in the target skill word frequency parameters at each time point are calculated and summed, assuming a weighted sum of the differences is obtained. Calculate the sum of the squares at each time point to obtain... Substitute the above data into the formula for calculating the attenuation slope parameter: ; ; ; ; ; The output result is approximately 46.49. The innovation of this formula lies in the introduction of the square of the sum of squares of time and the fourth power of the smoothing period into the denominator as a regularization penalty mechanism, which effectively filters out the short-term data fluctuations caused by seasonal recruitment by enterprises.

[0030] S313: Call the attenuation slope parameter to analyze the evolution trend of the target skill word frequency parameter, determine the changes in enterprise demand for various professional skills, and establish a skill demand evolution record; The calculated attenuation slope parameter, 46.49, is used to analyze the evolution trend of the target skill's word frequency parameter dropping from a peak of 1500 to 300. A baseline is established: when the calculated attenuation slope parameter value is greater than or equal to 40, the enterprise's demand for this professional skill is judged to be in an accelerated elimination cycle; when the value is between 20 and 40, it is judged to be in a slow cooling cycle; and when the value is less than 20, it is judged to be in a normal fluctuation and equilibrium cycle. Based on the value of 46.49, since it is greater than 40, the judgment logic identifies a negative evolution trend of a sharp shrinkage in the demand for the target skill, and judges that among the changes in the demand for various professional skills, this specific skill no longer has core market value. A skill demand evolution record is established. A JSON format record with the English abbreviation of the skill as the document name is created in the distributed document database. The evolution trend status identifier "accelerated elimination" and the attenuation slope parameter value "46.49" are written into this record, along with a timestamp tag for high-risk obsolescence warning.

[0031] Please see Figure 5 The specific process for obtaining the underlying capability compensation record is as follows: S411: Obtain the industry elimination threshold, retrieve the skill requirement evolution record, extract the skill requirement decay slope parameter, compare the industry elimination threshold with the skill requirement decay slope parameter, determine the over-limit state, and extract matching feature values ​​based on the over-limit state attributes to generate skill assessment records. To obtain historical word frequency evolution data sequences of obsolete occupational skills from authorized public statistical data, specifically by accessing the national-level employment guidance database and downloading historical recruitment demand data sets for 15 programming languages ​​explicitly marked as "out of mainstream application" over the past ten years. Extract the tail time slices of the aforementioned historical word frequency evolution data sequences in descending order, sort the sequences chronologically from most recent to oldest, and extract data points from the last 12 months before the obsolescence declaration as the tail time slices. Perform linear regression analysis on these 12-month tail time slices to obtain a set of historical delisting slopes, calculating an array of decay slope values ​​for these 15 obsolete skills. Calculate the set mean and standard deviation of this historical delisting slope set. Assuming the calculated set mean is 55 and the standard deviation is 12, subtract the standard deviation from the set mean of 55 to output a lower limit boundary value of 43, which is used as the industry elimination threshold. Retrieve the skill demand evolution records previously stored in the database and extract the skill demand decay slope parameter value of 46.49 from these records. The industry elimination threshold value of 43 is compared with the skill demand decay slope parameter value of 46.49. A logical comparison operation is performed. Since 46.49 is greater than 43, a logical judgment switch is triggered, and the out-of-limit status is determined to be "exceeded the elimination threshold". Based on this out-of-limit status attribute, matching feature values ​​are extracted from a preset feature mapping table, such as extracting the status code "0" or the depreciation warning string. The comparison values, the out-of-limit status, and the extracted matching feature values ​​are written into a relational data table to generate a skill assessment record.

[0032] S412: Call the skill assessment record, extract the preset loss ratio coefficient, retrieve the quantitative value inside the skill assessment record, multiply the quantitative value and the preset loss ratio coefficient, output the assessment loss product term, adjust the skill assessment record to deduct the product term, and obtain the overflow parameter; The system retrieves the skill assessment records created in the relational database table. It performs an algebraic subtraction operation between the extracted skill demand decay slope parameter (46.49) and the calculated industry elimination threshold (43), subtracting 43 from 46.49 to output the deviation difference value (3.49). The absolute value of this deviation difference (3.49) is obtained. This absolute value (3.49) is divided by the absolute value of the industry elimination threshold (43) using a floating-point division operation to generate a relative decay ratio parameter, approximately 0.0811. Using the natural constant e (approximately 2.718) as the base and the negative of the relative decay ratio parameter 0.0811 (-0.0811) as the exponent, an exponential function is performed to extract the decay factor and calculate... The output value is approximately 0.922. Subtracting this attenuation factor from the value one yields an output of 0.078. This value of 0.078 is extracted as the preset depreciation ratio coefficient. The quantitative value within the skill assessment record is retrieved. This quantitative value represents the target user's original ability assessment score for this declining skill on the training platform, assumed to be 85 points. The quantitative value 85 is multiplied by the extracted preset depreciation ratio coefficient 0.078, outputting a depreciation product value of 6.63. This quantitative value is adjusted to subtract the output depreciation product term, i.e., performing a subtraction operation of 85 minus 6.63, resulting in an overflow parameter value of 78.37. This series of nonlinear exponential mappings and multiplication-subtraction logic implements a continuous penalty mechanism for skill effectiveness.

[0033] Table 2 Calculation and Deduction Test Table for Dynamic Loss Ratio As shown in Table 2, the exponential decay calculation process under three different slope states is listed, clearly demonstrating the data flow relationship that the greater the deviation from the limit, the higher the relative ratio parameter, which ultimately leads to a significant reduction in the overflow parameter.

[0034] S413: Based on the overflow parameter, obtain the capability rating parameter table, extract the user's basic capability rating value, perform an addition operation on the overflow parameter and the basic capability rating value, output the compensation sum parameter, combine the personnel identity identifier, configure the association mapping attribute between the compensation sum parameter and the personnel identity identifier, and establish the underlying capability compensation record. Based on the calculated overflow parameter value of 78.37, the capability rating parameter table in CSV format stored on the local server is retrieved. The user's ID is used to search the parameter table, extracting the user's basic capability rating value in areas unaffected by the lifecycle of specific technical frameworks, such as general algorithmic logic and data structured thinking. Assuming the extracted basic capability rating value is 65, the attenuated overflow parameter value of 78.37 and the extracted basic capability rating value of 65 are loaded into the central processing unit's registers, and a floating-point addition operation is performed: 78.37 plus 65, outputting a compensation total parameter value of 143.37. Combined with the globally unique user identifier assigned during registration (such as a hash string containing 18 digits and letters), an association mapping attribute is configured in the Redis cache database between the compensation total parameter value of 143.37 and the user identifier, forming a one-to-one key-value pair. This mapping data is persistently written to the underlying distributed storage system, establishing an underlying capability compensation record file.

[0035] Please see Figure 6 The specific process for obtaining a multi-dimensional profile of a user's professional capabilities is as follows: S511: Analyze the internship work logs of target users, extract non-standard business instructions, analyze the standard documents of job requirements, extract standard control nodes, perform set intersection operations on non-standard business instructions and standard control nodes, filter and match character feature items, aggregate overlapping text fragments to configure logical mapping key-value pairs, and establish a common node set; The system retrieves daily plain text internship logs uploaded by the target user during their internship period from the local storage system. It scans and extracts descriptions of unconventional operations containing verb-object phrases not explicitly listed in the standard development manual, such as extracting text fragments like "bypassing the cache to directly access the underlying view" and "manually concatenating cross-domain request headers" as non-standard business instructions. It analyzes the standard job requirement documents provided by the enterprise's human resources system, extracting standard control nodes using rule-based keyword extraction techniques, such as standardized definitions like "database transaction isolation requirements" and "application-layer anti-injection review mechanism." In memory, it performs a set intersection operation on the extracted set of non-standard business instruction strings and the set of standard control node strings. During the intersection operation, a cosine similarity-based semantic matching algorithm is introduced to calculate the text vector similarity between the two sets. When the similarity score between two text fragments is greater than a set matching threshold of 0.85, the matching character features are selected. Aggregate semantically overlapping internship log operation fragments with job standard specification fragments, configure logical mapping key-value pairs, such as configuring a key-value combination with the key "non-standard cache bypass action" and the value "standard data consistency control rule", and establish a common node set at the data structure level.

[0036] S512: Call the shared node set, count the frequency attributes of text-level nodes, combine them with weight coefficients to calculate node feature parameters, obtain the preset conversion ratio, calculate the product of feature parameters and conversion ratio, and generate alternative indicators; The shared node set data structure established in memory space is invoked. The frequency attribute of text-level nodes contained in each shared node is statistically analyzed, that is, the total number of times the intersection event of each specific mapping relationship appears repeatedly in all internship work logs. For example, the shared node "cache bypass and consistency control" appeared a total of 24 times in the logs over four consecutive months. A weight coefficient is introduced, which is dynamically assigned based on the level of standard control nodes in the enterprise's core architecture. A weight of 1.5 is set for control nodes involving underlying data security, and a weight of 0.8 is set for control nodes involving ordinary page rendering. Here, for the aforementioned database consistency control event, its weight coefficient is set to 1.5. An algebraic multiplication operation is performed between the node frequency attribute value of 24 and the weight coefficient of 1.5 to calculate the node feature parameter value of 36. A conversion ratio preset in the configuration file is read. This ratio is calculated based on historical periodic data statistics of junior engineer training in the same industry transforming into independent business leaders, and is used to convert business frequency into capability scores. In this embodiment, the obtained conversion ratio value is selected as 0.5. The node feature parameter value of 36 and the conversion ratio of 0.5 are loaded into the computing unit to perform a product operation, that is, 36 is multiplied by 0.5 to generate a substitute index with a value of 18.

[0037] S513: Call the alternative indicators, read the assessment indicators of the test reshaping assessment record, retrieve the underlying ability compensation record to retrieve the summation parameters, perform structural standardization alignment on the alternative indicators, assessment indicators and summation parameters, combine vector concatenation to output three-dimensional feature vectors, configure multi-dimensional feature space coordinate mapping relationship, construct global data topology structure, and establish a multi-dimensional profile of user professional ability. In the main control program's runtime environment, the calculated alternative indicator value of 18 is invoked. Using a database retrieval command, the assessment indicator value of 3169.485 from the test reconstruction assessment record in the non-relational database is retrieved. Simultaneously, the underlying capability compensation record in the distributed storage system is retrieved, and the previously calculated compensation summation parameter value of 143.37 is retrieved through consistent user ID matching. Structural standardization and alignment are performed on the obtained alternative indicator (representing non-standard business experience transformation), assessment indicator (representing code quality improvement capability), and summation parameter (representing the underlying logic foundation against technical degradation). Specifically, the alignment process uses a Min-Max extreme value normalization algorithm to compress the three values ​​of different orders of magnitude and physical meanings into a standard floating-point range of 0 to 1, ensuring equal weight in subsequent calculations. The three normalized floating-point numbers are then used as elements, and a vector concatenation operation is performed sequentially in memory to output a continuous one-dimensional and three-dimensional feature vector array, such as [0.45, 0.72, 0.68]. In a three-dimensional Cartesian coordinate system, the three elements of the three-dimensional feature vector are used as the coordinate inputs for the X, Y, and Z axes, respectively. A multi-dimensional feature space coordinate mapping relationship is configured to establish a unique spatial data point in the three-dimensional space. Centered on this spatial data point, sub-level label data tables of various dimensions are radiated outwards, constructing a hierarchical and closely interconnected global data topology. Finally, the user's multi-dimensional professional competence profile is rendered and output in the front-end visualization system.

[0038] A system for constructing a multi-dimensional profile of vocational abilities for employment guidance, comprising the following components: (The system is used to execute the aforementioned method for constructing a multi-dimensional profile of vocational abilities for employment guidance.) The project comparison and analysis module extracts the character sequence of the source code of the user's training project and the character sequence of the open source template in the open source resource library, compares the overlap status, calculates the total intersection and code overlap ratio parameters, and generates source code overlap analysis records. The assessment results analysis module analyzes source code overlap parsing records, analyzes project source code to extract custom variables and exception handling nodes to calculate total parameters, combines basic theoretical scores to calculate the product, uses code overlap ratio parameters to adjust the product to obtain assessment indicators, and generates test reshaping assessment records. The Skills Evolution Analysis module analyzes historical recruitment texts to obtain target skill word frequency parameters, calculates and minimizes the sum of squared errors in the target skill word frequency time series, optimizes the minimization of the sum of squared errors and performs linear regression fitting, calculates the skill demand decay slope, and outputs a record of skill demand evolution. The capability compensation calculation module obtains the industry elimination threshold, compares it with the skill requirement decay slope parameter in the skill requirement evolution record, obtains the skill assessment record, deducts the product of the loss ratio to generate the overflow parameter, and sums it with the basic capability rating to generate the underlying capability compensation record. The user profile building module extracts non-standard business instructions from user internship work logs, extracts and compares standard control nodes with job requirements standards, calculates the product of feature parameters and conversion ratios to obtain alternative indicators, and constructs a multi-dimensional profile of user professional abilities by combining test-oriented reshaping assessment records and underlying ability compensation records.

[0039] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for constructing a multi-dimensional profile of vocational abilities for employment guidance, characterized in that, Includes the following steps: S1: Extract the character sequence of the source code of the user's training project and the character sequence of the open source template in the open source resource library, compare the overlap status, calculate the total intersection and code overlap ratio parameters, and generate source code overlap parsing records; S2: Analyze the source code overlap parsing record, analyze the project source code to extract custom variables and exception handling nodes to calculate the total parameters, combine the basic theoretical scores to calculate the product, use the code overlap ratio parameter to adjust the product to obtain the assessment indicators, and generate the test reshaping assessment record; S3: Analyze historical recruitment texts to obtain the target skill word frequency parameter, calculate and minimize the sum of squared errors in the target skill word frequency time series, optimize the minimization of the sum of squared errors and perform linear regression fitting, calculate the skill demand decay slope, and output the skill demand evolution record. S4: Obtain the industry elimination threshold, compare it with the skill requirement decay slope parameter in the skill requirement evolution record, obtain the skill assessment record, deduct the product of the loss ratio to generate the overflow parameter, and sum it with the basic ability rating to generate the underlying ability compensation record. S5: Extract non-standard business instructions from the user's internship work log, extract standard control nodes by combining them with job requirement standards and compare them, calculate the product of feature parameters and conversion ratio to obtain alternative indicators, and construct a multi-dimensional profile of the user's professional ability by combining the test reshaping assessment record and the underlying ability compensation record.

2. The method for constructing a multi-dimensional profile of vocational abilities for employment guidance according to claim 1, characterized in that, The source code overlap analysis record includes an original logic fragment table, template dependency tags, and a similar file index list. The exam reshaping assessment record includes a practical net value score, an engineering standardization index, and a theoretical water content stripping sheet. The skill demand evolution record includes a technology life cycle prediction table, a market popularity downgrade list, and a framework obsolescence warning card. The underlying capability compensation record includes a skill transfer conversion detail, a basic potential assessment card, and a core competency minimum score. The user's multi-dimensional professional capability profile includes an employment competitiveness radar chart, a job suitability dashboard, and a weakness advancement roadmap.

3. The method for constructing a multi-dimensional profile of vocational abilities for employment guidance according to claim 1, characterized in that, The specific process for obtaining the source code overlap parsing record is as follows: S111: Obtain the authorized public sample dataset, retrieve the local training project data frame, scan the text content in the training project data frame to extract the source code character sequence, connect to the open source resource library to extract the open source template character sequence, perform format standardization conversion and deduplication filtering on the source code character sequence and the open source template character sequence, map the text feature vector dimension according to the text content length, and generate a sequence alignment matrix; S112: Call the sequence alignment matrix to extract the character sequence of the project source code and the character sequence of the open source template, monitor the overlapping state attribute of the character sequence of the project source code and the character sequence of the open source template in the vector space, start the character traversal and comparison instruction to determine the matching text node, extract the position coordinates of the matching text node, accumulate the count value of the matching text node according to the increment rule, and generate the total number of intersection elements. S113: Call the total number of intersection elements, detect the length allocation status value for the open source template character sequence, extract the ratio calculation benchmark value, perform scalar division operation to process the total number of intersection elements and the length allocation status value, output the code overlap ratio parameter, aggregate the total number of intersection elements and the code overlap ratio parameter to configure associated key-value pairs, map the associated data topology, and establish source code overlap parsing records.

4. The method for constructing a multi-dimensional profile of vocational abilities for employment guidance according to claim 3, characterized in that, The specific process for obtaining the test-oriented reshaping assessment record is as follows: S211: Analyze the source code overlap parsing record, extract the code overlap ratio parameter, retrieve the project source code character sequence and read the custom variable statement and exception handling node respectively, perform a counting operation on the custom variable statement to obtain the statement quantity, count the exception handling node to obtain the node quantity, perform arithmetic summation on the statement quantity and the node quantity to generate the summation parameter; S212: Call the basic theoretical score from the database, multiply the basic theoretical score by the summation parameter, and generate the product parameter; S213: Collect the number of lines in the life domain of custom variable statements, detect the number of skipped lines in exception handling nodes, obtain the baseline line count, and obtain the total number of matches, using the following formula: ； Calculate and obtain assessment indicators, and establish test-oriented re-assessment records; in, The aforementioned assessment indicators, For product parameters, This is a parameter representing the code overlap ratio. Based on the base row number, To match the total number, This is the cumulative index variable for the node. For index The number of rows in the survival domain, For index The number of lines jumped.

5. The method for constructing a multi-dimensional profile of vocational abilities for employment guidance according to claim 4, characterized in that, The specific process for obtaining the skill requirement evolution record is as follows: S311: Analyze the company's historical recruitment texts, extract multiple categories of professional skill characters, perform word segmentation comparison and traversal counting on the extracted professional skill characters in the text sequence after time series segmentation, accumulate the occurrence frequency of each character, and generate target skill word frequency parameters; S312: Call the target skill word frequency parameter, obtain the sampling time node and matching base, calculate the error sum of squares minimization state, extract the smoothing period and confidence factor to optimize the error sum of squares minimization state and perform linear regression fitting, collect the total span, and calculate the decay slope parameter; S313: Call the attenuation slope parameter to analyze the evolution trend of the target skill word frequency parameter, determine the changes in enterprise demand for various professional skills, and establish a skill demand evolution record.

6. The method for constructing a multi-dimensional profile of vocational abilities for employment guidance according to claim 5, characterized in that, The specific process for obtaining the underlying capability compensation record is as follows: S411: Obtain the industry elimination threshold, retrieve the skill requirement evolution record, extract the skill requirement decay slope parameter, compare the industry elimination threshold with the skill requirement decay slope parameter, determine the over-limit state, and extract matching feature values ​​based on the over-limit state attributes to generate a skill assessment record. S412: Call the skill assessment record, extract the preset loss ratio coefficient, retrieve the quantitative value inside the skill assessment record, multiply the quantitative value and the preset loss ratio coefficient, output the assessment loss product term, adjust the skill assessment record to deduct the product term, and obtain the overflow parameter; S413: Based on the overflow parameter, obtain the capability rating parameter table, extract the user's basic capability rating value, perform an addition operation between the overflow parameter and the basic capability rating value, output the compensation sum parameter, combine the personnel identity identifier, configure the association mapping attribute between the compensation sum parameter and the personnel identity identifier, and establish the underlying capability compensation record.

7. The method for constructing a multi-dimensional profile of vocational abilities for employment guidance according to claim 6, characterized in that, The process of obtaining the industry elimination threshold is as follows: Obtain the historical word frequency evolution data sequence of obsolete occupational skills from authorized public statistical data, extract the tail time slices of the historical word frequency evolution data sequence in descending order, perform linear regression analysis on the tail time slices to obtain the historical delisting slope set, calculate the set mean and standard deviation of the historical delisting slope set, and output the lower limit boundary value as the industry elimination limit by subtracting the standard deviation from the set mean. The process of extracting the preset loss ratio coefficient is as follows: Perform an algebraic subtraction operation on the skill demand attenuation slope parameter and the industry elimination limit to output the excess deviation difference. Obtain the absolute value of the excess deviation difference. Divide the absolute value by the absolute value of the industry elimination limit to generate a relative decay ratio parameter. Perform an exponential function operation with the natural constant as the base and the negative number of the relative decay ratio parameter as the exponent to extract the decay factor. Subtract the decay factor from 1 to output the preset depreciation ratio coefficient.

8. The method for constructing a multi-dimensional profile of vocational abilities for employment guidance according to claim 6, characterized in that, The specific process for obtaining the multi-dimensional profile of a user's professional abilities is as follows: S511: Analyze the internship work logs of target users, extract non-standard business instructions, analyze the standard documents of job requirements, extract standard control nodes, perform set intersection operations on non-standard business instructions and standard control nodes, filter and match character feature items, aggregate overlapping text fragments to configure logical mapping key-value pairs, and establish a common node set; S512: Call the shared node set, count the frequency attributes of text-level nodes and calculate the node feature parameters by combining them with the weight coefficients, obtain the preset conversion ratio, calculate the product of the feature parameters and the conversion ratio, and generate alternative indicators. S513: Call the alternative indicators, read the assessment indicators of the test reshaping assessment record, retrieve the summation parameters from the underlying ability compensation record, perform structural standardization alignment on the alternative indicators, assessment indicators and summation parameters, combine vector concatenation to output a three-dimensional feature vector, configure the multi-dimensional feature space coordinate mapping relationship, construct a global data topology structure, and establish a multi-dimensional profile of the user's professional ability.

9. A system for constructing a multi-dimensional profile of vocational abilities for employment guidance, characterized in that, The system is used to implement the method for constructing a multi-dimensional profile of vocational abilities for employment guidance as described in any one of claims 1-8, and the system comprises: The project comparison and analysis module extracts the character sequence of the source code of the user's training project and the character sequence of the open source template in the open source resource library, compares the overlap status, calculates the total intersection and code overlap ratio parameters, and generates source code overlap analysis records. The assessment result analysis module analyzes the source code overlap parsing record, analyzes the project source code to extract custom variables and calculate the total parameters of exception handling nodes, calculates the product with the basic theory score, adjusts the product using the code overlap ratio parameter to obtain the assessment index, and generates the test reshaping assessment record. The Skills Evolution Analysis module analyzes historical recruitment texts to obtain target skill word frequency parameters, calculates and minimizes the sum of squared errors in the target skill word frequency time series, optimizes the minimization of the sum of squared errors and performs linear regression fitting, calculates the skill demand decay slope, and outputs a record of skill demand evolution. The capability compensation calculation module obtains the industry elimination threshold, compares it with the skill requirement decay slope parameter of the skill requirement evolution record, obtains the skill assessment record, deducts the product of the loss ratio to generate an overflow parameter, and sums it with the basic capability rating to generate the underlying capability compensation record. The user profile building module extracts non-standard business instructions from the user's internship work log, extracts and compares standard control nodes with job requirement standards, calculates the product of feature parameters and conversion ratio to obtain alternative indicators, and constructs a multi-dimensional profile of the user's professional ability by combining the test reshaping assessment record and the underlying ability compensation record.

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