Multi-dimensional intelligent matching method and system for employment post

Abstract

The invention is suitable for the technical field of post matching, and provides a multi-dimensional intelligent matching method and system for employment posts, and the method comprises the following steps: carrying out the comprehensive skill matching of posts and talents, and determining a comprehensive skill matching score; calling educational background information and certificate information in the talent resume, and reasoning to determine the learning ability of the talent; extracting post skills which are matched unsuccessfully in the employment posts, determining the demand emergency degree and learning difficulty of each post skill which is matched unsuccessfully, and determining the association degree between the post skills and talent skills and the association degree between the post skills and professions; and based on the learning ability, the demand emergency degree, the learning difficulty and the correlation degree, determining the cultivation cost cardinal number of each post skill which fails in matching. According to the invention, the learning ability of talents is incorporated into a matching evaluation system, so that potential suitable talents with relatively strong learning ability and development potential although the current skills are not completely matched with posts can be identified, the talent selection range is expanded for enterprises, and the talent recruitment efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of job matching technology, specifically to a multi-dimensional intelligent matching method and system for employment positions. Background Technology

[0002] Traditional job matching methods primarily rely on simple keyword matching or manual resume screening, which has several limitations. Firstly, they fail to fully consider the dynamic changes in job skill requirements, and the impact of market supply and demand on the importance of job skills is not effectively reflected, leading to inaccurate matching results. Secondly, traditional methods often neglect the learning ability of talent and the trainability of job skills. For positions where a match is unsuccessful but the talent possesses learning potential and the job skills can be acquired through training, there is a lack of effective evaluation and handling mechanisms. Therefore, a multi-dimensional intelligent job matching method and system is needed to address these problems. Summary of the Invention

[0003] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a multi-dimensional intelligent matching method and system for employment positions, so as to solve the problems existing in the above-mentioned background technology.

[0004] This invention is implemented as follows: a multi-dimensional intelligent matching method for employment positions, the method comprising the following steps: The system performs comprehensive skills matching between job positions and employees. Each job skill has a corresponding dynamic weight, which is adjusted in real time according to market supply and demand to determine the comprehensive skills matching score. By retrieving educational and certification information from candidates' resumes, their learning abilities can be inferred and determined. Extract the job skills that fail to match the job requirements, determine the urgency and learning difficulty of each job skill that fails to match, and determine the correlation between the job skills and the skills and majors of the personnel. The base cost of training each job skill that fails to match is determined based on learning ability, urgency of need, learning difficulty, and relevance, and the base cost of training the job is determined accordingly. The comprehensive matching index is determined based on the comprehensive skills matching score and the base of total job training cost.

[0005] As a further aspect of the present invention, the step of adjusting the dynamic weights in real time according to market supply and demand specifically includes: By capturing demand data and talent supply data for the skills of each job position through recruitment platforms, the demand index and supply index for the skills of each job position are calculated. Calculate the weighted base for each job skill. Weighted base = demand index / supply index. Demand index = job demand / total demand. Supply index = talent availability / total supply. Update the dynamic weight of each job skill. The dynamic weight is equal to the base weight of the job skill / the sum of the base weights of all job skills.

[0006] As a further aspect of the present invention, the step of determining a person's learning ability through reasoning specifically includes: The scores for academic qualifications, school reputation, certificate level, certificate authority, and certificate pass rate are determined based on academic information and certificate information. Learning ability is calculated based on scores for academic qualifications, school reputation, certificate level, certificate authority, and certificate pass rate.

[0007] As a further aspect of the present invention, the step of determining the correlation between the job skills and the skills and professions of the personnel specifically includes: Determine the skill level (Level 1, Level 2, and Level 3) to which the job skills belong; Determine the professional level 1, professional level 2, and professional level 3 to which the job skills belong; Determine the highest skill level and highest professional level that the talent skills and professional skills meet, and calculate the correlation based on the highest skill level and highest professional level.

[0008] As a further aspect of the present invention, the step of determining the base cost of training each unmatched job skill based on learning ability, urgency of demand, learning difficulty, and relevance, and determining the base cost of total job training, specifically includes: Calculate the base cost C for training each job skill that fails to match, C=(U×D) / (α×L+β×R), where U represents the urgency of the need, D represents the difficulty of learning, L represents the learning ability, R represents the relevance, and α and β are condition coefficients; Determine the base for total job training cost; the base for total job training cost = There are a total of n job skills that failed to match. This represents the dynamic weight of the job skill for the i-th unmatched job. This represents the base cost of training the job skill for the i-th unmatched position.

[0009] As a further aspect of the present invention, when determining the comprehensive skill matching score, the successfully matched job skills are first determined, and then the comprehensive skill matching score is calculated. The comprehensive skill matching score is the sum of the dynamic weights corresponding to the successfully matched job skills / the sum of the dynamic weights corresponding to all job skills.

[0010] Another objective of this invention is to provide a multi-dimensional intelligent matching system for employment positions, the system comprising: The comprehensive skills matching module is used to match job positions with candidates based on their comprehensive skills. Each job skill has a corresponding dynamic weight, which is adjusted in real time according to market supply and demand to determine the comprehensive skills matching score. The learning ability determination module is used to retrieve educational and certificate information from candidates' resumes and infer and determine their learning abilities. The matching failure analysis module is used to extract job skills that failed to match in the job positions, determine the urgency of demand and learning difficulty of each job skill that failed to match, and determine the correlation between the job skills and the talent skills and majors. The training cost calculation module is used to determine the training cost base for each job skill that fails to match based on learning ability, urgency of demand, learning difficulty, and relevance, and to determine the total training cost base for the job. The comprehensive matching index module is used to determine the comprehensive matching index based on the comprehensive skills matching score and the total cost of job training.

[0011] As a further aspect of the present invention, the integrated skills matching module includes: The supply and demand index calculation unit is used to capture demand data and talent supply data for each job skill through the recruitment platform, and calculate the demand index and supply index for each job skill. The weighted base calculation unit is used to calculate the weighted base of each job skill. The weighted base = demand index / supply index. The demand index = job demand / total demand. The supply index = talent availability / total supply. The dynamic weight determination unit is used to update the dynamic weight of each job skill. The dynamic weight is calculated as: dynamic weight = weight base of the job skill / sum of weight bases of all job skills.

[0012] As a further aspect of the present invention, the learning ability determination module includes: The academic certificate analysis unit is used to determine the academic score, school reputation score, certificate level score, certificate authority score, and certificate pass rate score based on academic information and certificate information. The learning ability calculation unit is used to calculate learning ability based on academic qualifications, school reputation, certificate level, certificate authority, and certificate pass rate.

[0013] As a further aspect of the present invention, the matching failure analysis module includes: The skill type determination unit is used to determine the skill level 1, skill level 2, and skill level 3 of the job skill. The professional type determination unit is used to determine the professional level 1 type, professional level 2 type, and professional level 3 type to which the job skills belong; The correlation calculation unit is used to determine the highest skill level and the highest professional level that the talent's skills and professions meet, and to calculate the correlation based on the highest skill level and the highest professional level.

[0014] Compared with the prior art, the beneficial effects of the present invention are: This invention, by assigning dynamic weights to each job skill and adjusting them in real time based on market supply and demand, more accurately reflects the importance of job skills in the current market environment. This makes the overall skill matching score between jobs and talents more aligned with actual needs, improving the accuracy of the matching. By retrieving educational and certification information from candidates' resumes to infer learning ability, and incorporating this ability into the matching evaluation system, it can identify potential suitable talents who, although their current skills do not perfectly match the job requirements, possess strong learning abilities and development potential. This expands the talent selection scope for enterprises and improves the efficiency of talent recruitment. Furthermore, it extracts job skills that fail to match and, by comprehensively considering the urgency of the need, the difficulty of learning, and the relevance to the candidate's skills and major, determines the training cost for each failed-match job skill, thus obtaining the total training cost for the job. This provides enterprises with cost information on talent development, helping them weigh the cost-effectiveness between directly recruiting suitable talent and cultivating potential talent when making recruitment decisions, leading to more scientific and rational decisions. Attached Figure Description

[0015] Figure 1 This is a flowchart of a multi-dimensional intelligent matching method for employment positions.

[0016] Figure 2 This is a flowchart illustrating the real-time adjustment of dynamic weights in a multi-dimensional intelligent matching method for job positions.

[0017] Figure 3 This is a flowchart for determining the learning ability of talent in a multi-dimensional intelligent matching method for employment positions.

[0018] Figure 4 This is a flowchart for determining the correlation degree in a multi-dimensional intelligent matching method for employment positions.

[0019] Figure 5 This is a flowchart for determining the base of total job training cost in a multi-dimensional intelligent matching method for employment positions.

[0020] Figure 6 This is a schematic diagram of a multi-dimensional intelligent matching system for employment positions. Detailed Implementation

[0021] 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 specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0022] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0023] like Figure 1 As shown in the figure, this embodiment of the invention provides a multi-dimensional intelligent matching method for employment positions, the method including the following steps: S100 is used to match job positions with employees based on their comprehensive skills. Each job skill has a corresponding dynamic weight, which is adjusted in real time according to market supply and demand to determine the comprehensive skills matching score. S200 retrieves educational and certification information from a candidate's resume to infer and determine their learning ability. S300, extract the job skills that failed to match in the job positions, determine the urgency of demand and learning difficulty of each job skill that failed to match, and determine the correlation between the job skills and talent skills and majors; S400 determines the base cost of training each job skill that fails to match, based on learning ability, urgency of need, learning difficulty, and relevance, and determines the base cost of total job training. S500 determines the comprehensive matching index based on the comprehensive skills matching score and the base of total job training cost.

[0024] It should be noted that traditional matching methods typically use fixed weights for job skills, failing to consider the dynamic impact of market supply and demand on the importance of job skills. For job skills that fail to match, traditional methods do not further analyze the urgency of demand, learning difficulty, and relevance to the candidate's skills and major, thus failing to determine the cost of developing these skills. This leaves companies lacking comprehensive information when making recruitment decisions, potentially missing out on candidates with high potential but reasonable training costs, even if their current fit is not high. The embodiments of this invention aim to solve the above problems.

[0025] In this embodiment of the invention, traditional skill matching is first performed based on the job skill requirement information and the skill information in the candidate's resume. One job position corresponds to one or more job skills. It is important to note that each job skill in this embodiment has a dynamic weight, the specific value of which is adjusted in real time according to market supply and demand. This more accurately reflects the importance of job skills in the current market environment, making the overall skill matching score between the job and the candidate more closely aligned with actual needs and greatly improving the accuracy of the matching. When calculating the overall skill matching score, the successfully matched job skills are first identified, and then the overall skill matching score is calculated as follows: Overall Skill Matching Score = Sum of the dynamic weights corresponding to the successfully matched job skills / Sum of the dynamic weights corresponding to all job skills.

[0026] Next, the system retrieves educational and certification information from candidates' resumes to infer their learning abilities. This learning ability is then incorporated into the matching assessment system, identifying potential suitable candidates who, while their current skills may not perfectly match the job requirements, possess strong learning abilities and development potential. This expands the company's talent selection pool and improves the quality of recruitment. Furthermore, the system extracts job skills that are not matched to the advertised job requirements, determining the urgency and learning difficulty of each skill. The urgency and learning difficulty of each skill need to be pre-defined by the company's HR department. Simultaneously, the system determines the relevance between these job skills and the candidate's skills and major. This allows the system to determine the base cost of developing each unmatched job skill based on learning ability, urgency, learning difficulty, and relevance, thereby determining the total base cost of developing that job. This provides companies with cost information on talent development, helping them weigh the cost-benefit of directly recruiting suitable candidates versus developing potential talent, leading to more scientific and rational decisions. Finally, the comprehensive matching index is determined based on the overall skills matching score and the base of total job training cost. The comprehensive matching index is calculated as: Comprehensive Matching Index = K1 × Overall Skills Matching Score - K2 × Base of Total Job Training Cost, where K1 and K2 are fixed coefficients. This provides companies with a comprehensive evaluation indicator that can more accurately measure the overall match between the job and the candidate. Companies can use the comprehensive matching index to rank and screen candidates, improving recruitment efficiency.

[0027] like Figure 2 As shown, in a preferred embodiment of the present invention, the step of adjusting the dynamic weights in real time according to market supply and demand specifically includes: S101: By capturing the demand data and talent supply data of each job skill through the recruitment platform, the demand index and supply index of each job skill are calculated. S102, calculate the weight base of each job skill, weight base = demand index / supply index; S103, Update the dynamic weight of each job skill. Dynamic weight = weight base of the job skill / sum of weight bases of all job skills.

[0028] In this embodiment of the invention, to determine the dynamic weights, it is necessary to capture demand data and talent supply data for each job skill from a recruitment platform. Based on this data, the demand index and supply index for each job skill are calculated. The demand index = the demand for jobs containing that skill / the total demand for all jobs; the supply index = the number of talents holding that skill / the total supply of all talents. Then, the weight base for each job skill can be calculated: weight base = demand index / supply index. Finally, the dynamic weight for each job skill is obtained: dynamic weight = weight base for that job skill / the sum of the weight bases for all job skills corresponding to that job. To understand this, the sum of the dynamic weights of all job skills corresponding to each job is equal to 1. That is, for the same skill, the dynamic weight corresponding to that skill is different when it exists in different job positions.

[0029] like Figure 3 As shown in the preferred embodiment of the present invention, the step of determining a person's learning ability through reasoning specifically includes: S201 determines the score for academic qualifications, school reputation, certificate level, certificate authority, and certificate pass rate based on academic information and certificate information. S202 calculates learning ability based on academic qualifications, school reputation, certificate level, certificate authority, and certificate pass rate.

[0030] In this embodiment of the invention, the following are first determined based on the educational background and certificate information: educational background score, school reputation score, certificate level score, certificate authority score, and certificate pass rate score. Specifically, the educational background score can be defined as: PhD = 5, Master's = 4, Bachelor's = 3, Associate's = 2, High School = 1, and below High School = 0. The school reputation score can be determined based on the school ranking (such as QS ranking). The certificate level score can be defined as: Advanced Certificate = 3, Intermediate Certificate = 2, and Basic Certificate = 1. The certificate authority score can be assigned based on international certificates, national certificates, provincial certificates, and municipal certificates. The certificate pass rate score is obtained based on the pass rate of the certificate exam; the higher the pass rate, the lower the certificate pass rate score. Then, learning ability can be calculated based on the score of academic qualifications, school reputation, certificate level, certificate authority, and certificate pass rate. Learning ability = K3 × academic qualifications + K4 × school reputation + K5 × certificate level + K6 × certificate authority + K7 × certificate pass rate, where K3 to K7 are fixed coefficients.

[0031] like Figure 4As shown in a preferred embodiment of the present invention, the step of determining the correlation between the job skills and the skills and professions of the personnel specifically includes: S301, determine the skill level 1, skill level 2, and skill level 3 of the job skill; S302, determine the professional level 1 type, professional level 2 type, and professional level 3 type to which the job skills belong; S303 determines the highest skill level and highest professional level that the talent skills and professional skills meet, and calculates the correlation based on the highest skill level and highest professional level.

[0032] In this embodiment of the invention, a skill specialty type database is pre-established. This database includes Level 1, Level 2, Level 3, Level 1, Level 2, and Level 3 specialty types for each skill. For example, the skill "Industrial Robot Operation and Maintenance" corresponds to the following Level 1 skill type: Technical Operation; Level 2 skill type: Advanced Manufacturing Technology; Level 3 skill type: Industrial Robot Operation and Maintenance; Level 1 specialty type: Equipment Manufacturing; Level 2 specialty type: Electromechanical Equipment; and Level 3 specialty type: Industrial Robot Technology. The matching degree for Level 1 type is A, for Level 2 type it is B, and for Level 3 type it is C, where C > B > A. This embodiment of the invention determines the highest skill level and highest specialty level that the talent's skills and specialties match based on the talent's resume information. The correlation degree is calculated based on the highest skill level and highest specialty level. For example, if the highest skill level is Level 2 and the highest specialty level is Level 3, then the correlation degree = K8 × B + K9 × C, where K8 and K9 are fixed coefficients.

[0033] like Figure 5 As shown in the preferred embodiment of the present invention, the step of determining the base cost of training each unmatched job skill based on learning ability, urgency of demand, learning difficulty, and relevance, and determining the base cost of total job training, specifically includes: S401, calculate the base cost C for training skills of each failed job match, C=(U×D) / (α×L+β×R); S402, Determine the base for total job training cost, the base for total job training cost = .

[0034] In this embodiment of the invention, after learning ability, urgency of need, learning difficulty, and relevance are all determined, the base training cost for each unmatched job skill can be calculated. Specifically, C = (U × D) / (α × L + β × R), where U represents urgency of need, D represents learning difficulty, L represents learning ability, R represents relevance, and α and β are conditional coefficients. Finally, the base total job training cost is calculated: Base total job training cost = There are n job skills that failed to match. This represents the dynamic weight of the job skill for the i-th unmatched job. This represents the base cost of training the job skill for the i-th unmatched position.

[0035] like Figure 6 As shown in the figure, this embodiment of the invention also provides a multi-dimensional intelligent matching system for employment positions, the system comprising: The comprehensive skills matching module 100 is used to match job positions with talents based on their comprehensive skills. Each job skill has a corresponding dynamic weight, which is adjusted in real time according to market supply and demand to determine the comprehensive skills matching score. The learning ability determination module 200 is used to retrieve educational and certificate information from a candidate's resume and infer and determine the candidate's learning ability. The matching failure analysis module 300 is used to extract the job skills that failed to match in the job positions, determine the urgency of demand and learning difficulty of each job skill that failed to match, and determine the correlation between the job skills and the talent skills and majors. The training cost calculation module 400 is used to determine the training cost base for each failed job skill based on learning ability, urgency of demand, learning difficulty, and relevance, and to determine the total training cost base for the job. The Comprehensive Matching Index Module 500 is used to determine the comprehensive matching index based on the comprehensive skills matching score and the total cost of job training.

[0036] In a preferred embodiment of the present invention, the integrated skills matching module 100 includes: The supply and demand index calculation unit is used to capture demand data and talent supply data for each job skill through the recruitment platform, and calculate the demand index and supply index for each job skill. The weighted base calculation unit is used to calculate the weighted base of each job skill. The weighted base = demand index / supply index. The demand index = job demand / total demand. The supply index = talent availability / total supply. The dynamic weight determination unit is used to update the dynamic weight of each job skill. The dynamic weight is calculated as: dynamic weight = weight base of the job skill / sum of weight bases of all job skills.

[0037] In a preferred embodiment of the present invention, the learning ability determination module 200 includes: The academic certificate analysis unit is used to determine the academic score, school reputation score, certificate level score, certificate authority score, and certificate pass rate score based on academic information and certificate information. The learning ability calculation unit is used to calculate learning ability based on academic qualifications, school reputation, certificate level, certificate authority, and certificate pass rate.

[0038] In a preferred embodiment of the present invention, the matching failure analysis module 300 includes: The skill type determination unit is used to determine the skill level 1, skill level 2, and skill level 3 of the job skill. The professional type determination unit is used to determine the professional level 1 type, professional level 2 type, and professional level 3 type to which the job skills belong; The correlation calculation unit is used to determine the highest skill level and the highest professional level that the talent's skills and professions meet, and to calculate the correlation based on the highest skill level and the highest professional level.

[0039] The above description only details the preferred embodiments of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0040] It should be understood that although the steps in the flowcharts of the various embodiments of the present invention are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the various embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0041] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0042] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the disclosure in the specification and embodiments. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.

Claims

1. A multi-dimensional intelligent matching method for employment positions, characterized in that, The method includes the following steps: The system performs comprehensive skills matching between job positions and employees. Each job skill has a corresponding dynamic weight, which is adjusted in real time according to market supply and demand to determine the comprehensive skills matching score. By retrieving educational and certification information from candidates' resumes, their learning abilities can be inferred and determined. Extract the job skills that fail to match the job requirements, determine the urgency and learning difficulty of each job skill that fails to match, and determine the correlation between the job skills and the skills and majors of the personnel. The base cost of training each job skill that fails to match is determined based on learning ability, urgency of need, learning difficulty, and relevance, and the base cost of training the job is determined accordingly. The comprehensive matching index is determined based on the comprehensive skills matching score and the base of total job training cost.

2. The multi-dimensional intelligent matching method for employment positions according to claim 1, characterized in that, The steps for real-time adjustment of dynamic weights based on market supply and demand include: By capturing demand data and talent supply data for the skills of each job position through recruitment platforms, the demand index and supply index for the skills of each job position are calculated. Calculate the weighted base for each job skill. Weighted base = demand index / supply index. Demand index = job demand / total demand. Supply index = talent availability / total supply. Update the dynamic weight of each job skill. The dynamic weight is equal to the base weight of the job skill / the sum of the base weights of all job skills.

3. The multi-dimensional intelligent matching method for employment positions according to claim 1, characterized in that, The steps for determining a person's learning ability through reasoning specifically include: The scores for academic qualifications, school reputation, certificate level, certificate authority, and certificate pass rate are determined based on academic information and certificate information. Learning ability is calculated based on scores for academic qualifications, school reputation, certificate level, certificate authority, and certificate pass rate.

4. The multi-dimensional intelligent matching method for employment positions according to claim 1, characterized in that, The step of determining the correlation between the job skills and the skills and professions of the personnel specifically includes: Determine the skill level (Level 1, Level 2, and Level 3) to which the job skills belong; Determine the professional level 1, professional level 2, and professional level 3 to which the job skills belong; Determine the highest skill level and highest professional level that the talent skills and professional skills meet, and calculate the correlation based on the highest skill level and highest professional level.

5. The multi-dimensional intelligent matching method for employment positions according to claim 1, characterized in that, The steps for determining the base cost of training each unmatched job skill, based on learning ability, urgency of need, learning difficulty, and relevance, and for determining the base cost of job training, specifically include: Calculate the base cost C for training each job skill that fails to match, C=(U×D) / (α×L+β×R), where U represents the urgency of the need, D represents the difficulty of learning, L represents the learning ability, R represents the relevance, and α and β are condition coefficients; Determine the base for total job training cost; the base for total job training cost = There are a total of n job skills that failed to match. This represents the dynamic weight of the job skill for the i-th unmatched job. This represents the base cost of training the job skill for the i-th unmatched position.

6. The multi-dimensional intelligent matching method for employment positions according to claim 1, characterized in that, When determining the comprehensive skills matching score, first identify the successfully matched job skills, and then calculate the comprehensive skills matching score. The comprehensive skills matching score = the sum of the dynamic weights corresponding to the successfully matched job skills / the sum of the dynamic weights corresponding to all job skills.

7. A multi-dimensional intelligent matching system for employment positions, characterized in that, The system includes: The comprehensive skills matching module is used to match job positions with candidates based on their comprehensive skills. Each job skill has a corresponding dynamic weight, which is adjusted in real time according to market supply and demand to determine the comprehensive skills matching score. The learning ability determination module is used to retrieve educational and certificate information from candidates' resumes and infer and determine their learning abilities. The matching failure analysis module is used to extract job skills that failed to match in the job positions, determine the urgency of demand and learning difficulty of each job skill that failed to match, and determine the correlation between the job skills and the talent skills and majors. The training cost calculation module is used to determine the training cost base for each job skill that fails to match based on learning ability, urgency of demand, learning difficulty, and relevance, and to determine the total training cost base for the job. The comprehensive matching index module is used to determine the comprehensive matching index based on the comprehensive skills matching score and the total cost of job training.

8. The multi-dimensional intelligent matching system for employment positions according to claim 7, characterized in that, The integrated skills matching module includes: The supply and demand index calculation unit is used to capture demand data and talent supply data for each job skill through the recruitment platform, and calculate the demand index and supply index for each job skill. The weighted base calculation unit is used to calculate the weighted base of each job skill. The weighted base = demand index / supply index. The demand index = job demand / total demand. The supply index = talent availability / total supply. The dynamic weight determination unit is used to update the dynamic weight of each job skill. The dynamic weight is calculated as: dynamic weight = weight base of the job skill / sum of weight bases of all job skills.

9. The multi-dimensional intelligent matching system for employment positions according to claim 7, characterized in that, The learning ability determination module includes: The academic certificate analysis unit is used to determine the academic score, school reputation score, certificate level score, certificate authority score, and certificate pass rate score based on academic information and certificate information. The learning ability calculation unit is used to calculate learning ability based on academic qualifications, school reputation, certificate level, certificate authority, and certificate pass rate.

10. The multi-dimensional intelligent matching system for employment positions according to claim 7, characterized in that, The matching failure analysis module includes: The skill type determination unit is used to determine the skill level 1, skill level 2, and skill level 3 of the job skill. The professional type determination unit is used to determine the professional level 1 type, professional level 2 type, and professional level 3 type to which the job skills belong; The correlation calculation unit is used to determine the highest skill level and the highest professional level that the talent's skills and professions meet, and to calculate the correlation based on the highest skill level and the highest professional level.

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