Large model resume screening system and method for human industry

Abstract

The invention relates to the technical field of AI large models, in particular to a large model resume screening system and method for the human industry, and the system consists of a working post analysis module, a candidate resume analysis module, a matching degree analysis module and a personal information module. The working post analysis module is used for deeply analyzing and extracting key requirements of working posts; the candidate resume analysis module is used for comprehensively analyzing the uploaded candidate resumes and extracting key information; the matching degree analysis module is used for carrying out accurate matching degree analysis in combination with working post requirements and candidate information and giving a comprehensive ranking; the personal information module is used for the user to manage personal information and account setting; the large model resume screening system and method for the human industry have the beneficial effects that efficient screening and processing of a large number of resumes are achieved through an automation technology, so that the working pressure of a human resource department is remarkably reduced, and the screening efficiency is greatly improved.

Description

Technical Field

[0001] The present invention relates to the field of AI big model technology, and specifically to a big model resume screening system and method for the human resources industry. Background Art

[0002] In the current recruitment environment, the challenges faced by human resources departments in the resume screening process are becoming increasingly significant. With the popularity of online recruitment platforms and the convenience they provide, companies often receive a large number of resumes quickly once they post a job position. These resumes include both high-quality applicants who meet the job requirements and many resumes that do not meet the requirements or are randomly submitted, making the screening work extremely complicated. During the screening process, human resources specialists must comprehensively review the core elements of applicants, such as their educational background, work experience, and skills, while also strictly checking the authenticity of the resumes. In addition, in response to the special needs of different positions, human resources specialists need to have in-depth professional knowledge reserves in order to more accurately evaluate the suitability of applicants. This requires human resources specialists to not only have a wide range of industry knowledge, but also have a deep insight into the specific responsibilities and requirements of each position.

[0003] In summary, the challenges faced by the HR department in the resume screening process are comprehensive, including but not limited to processing a large number of resumes, verifying the authenticity of resumes, meeting the requirements of job expertise, and dealing with job-seeking skills of applicants. In order to effectively cope with these challenges, HR specialists need to have excellent professional qualities and a rigorous work attitude to ensure the efficiency and accuracy of the recruitment process. Summary of the invention

[0004] The purpose of the present invention is to provide a large-model resume screening system and method for the human resources industry to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a large-model resume screening system for the human resources industry, the system consisting of a job position analysis module, a candidate resume analysis module, a matching degree analysis module and a personal information module;

[0006] Job analysis module, used to deeply analyze and extract key requirements of jobs;

[0007] Candidate resume analysis module, which conducts comprehensive analysis on uploaded candidate resumes and extracts key information;

[0008] The matching analysis module combines job requirements and candidate information to conduct accurate matching analysis and give a comprehensive ranking;

[0009] The personal information module allows users to manage their personal information and account settings.

[0010] Preferably, the job position analysis module includes:

[0011] PI interface, used to receive and send data requests;

[0012] The large model analysis unit is used to deeply analyze the job description input by HR and accurately extract key information from it, covering educational background, work experience, professional skills, job responsibilities, required certificates and soft skills.

[0013] Preferably, the candidate resume analysis module includes:

[0014] Resume upload unit, supports batch import of resume files in PDF or DOCX format;

[0015] Resume preprocessing unit, which cleans, converts and standardizes resume data;

[0016] The large model analysis unit is used to conduct in-depth analysis of resume content and accurately extract key information of candidates, including but not limited to name, contact information, educational background, work experience, professional skills, previous positions, certificates held, personal soft skills and comprehensive evaluation.

[0017] Preferably, the matching degree analysis module includes:

[0018] A data extraction unit extracts formatted data sets related to selected positions and candidates from the database;

[0019] Big data analysis model, which conducts in-depth comparative analysis of detailed job requirements and each candidate's information, and linearly scores each dimension according to the set standards;

[0020] Scoring dimensions include academic background, work experience, professional skills, understanding of job responsibilities, certification status, and soft skills;

[0021] The weighted processing unit performs weighted processing on each score according to the preset weight to obtain the candidate's final score;

[0022] The sorting unit sorts the candidates according to their final scores, and the candidates with higher scores are displayed first.

[0023] Preferably, the human information module includes:

[0024] User identification ID display unit;

[0025] Password modification unit;

[0026] Contact update unit, supports updating of phone number, email address and other information;

[0027] Contact address modification unit.

[0028] A method for screening large model resumes in the human resources industry, including a large model resume screening system for the human resources industry, the method comprising the following steps:

[0029] Through the job analysis module, we can deeply analyze and extract the key requirements of the job;

[0030] Through the candidate resume analysis module, the uploaded candidate resumes are comprehensively analyzed to extract key information;

[0031] The matching analysis module combines job requirements and candidate information to conduct accurate matching analysis and give a comprehensive ranking;

[0032] The personal information module allows users to manage their personal information and account settings.

[0033] Preferably, the matching analysis module includes the following steps:

[0034] Linearly score candidates on various dimensions according to the set standards;

[0035] The scores of each item are weighted according to the preset weights to obtain the final score of the candidate;

[0036] Candidates are sorted based on their final scores, with candidates with higher scores being displayed first.

[0037] Preferably, the scoring dimensions include academic background, work experience, professional skills, understanding of job responsibilities, certification status, and soft skills.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] The large-model resume screening system and method for the human resources industry proposed in the present invention realizes efficient screening and processing of a large number of resumes through automation technology, thereby significantly reducing the workload of the human resources department and greatly improving screening efficiency. This enables HR personnel to focus more on in-depth evaluations of highly matched candidates rather than spending time on preliminary resume screening. With the analytical capabilities of large models, the present invention can more accurately identify and match job requirements with the skills and experience of candidates, effectively reducing errors and omissions that may occur during manual screening, thereby improving the accuracy of recruitment. In addition, the automated resume screening process can eliminate the bias caused by human factors and ensure that each candidate is treated fairly based on objective standards, thereby enhancing the fairness of recruitment. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 Developing flow charts for the present invention;

[0041] Figure 2 This is a schematic diagram of the core functions of the present invention. DETAILED DESCRIPTION

[0042] In order to make the purpose and technical solution of the present invention clearly and completely described, and the advantages more clearly understood, the embodiments of the present invention are further described in detail with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are part of the embodiments of the present invention, rather than all of the embodiments, and are only used to explain the embodiments of the present invention, and are not used to limit the embodiments of the present invention. All other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] Embodiment 1

[0044] See also Figure 1 to Figure 2 The present invention provides a technical solution: the human-machine interface is an important part of the system, and the interaction between the user and the system is realized through the main interface module. The users of the system are mainly divided into two categories: HR recruiters and administrators. They access the system interface through a browser and enter system pages with different functions and layouts according to different user types.

[0045] For HR recruiters, they mainly perform the following operations through the system interface: input the job requirements to be recruited so that the system can accurately understand and analyze the job requirements; upload candidate resumes for the system to conduct subsequent matching analysis; view comprehensive ranking information to quickly screen out the most suitable candidates. For security reasons, the system has designed strict permission management to ensure that different HR recruiter accounts can only view the candidate resume information uploaded by themselves, effectively protecting the security and privacy of data.

[0046] Administrators maintain the model library and database through the system maintenance page. This includes updating or adding new large models to adapt to changing recruitment needs, managing the database to ensure the accuracy and completeness of the data, and managing and maintaining the accounts of HR recruiters to ensure the reasonable allocation and management of user permissions.

[0047] The model library is one of the cores of this system. It is responsible for in-depth analysis of job requirements, candidate information and matching degree, and finally gives a score ranking. At present, the system supports calling multiple advanced large models such as Inspur Yunhai Ruoda Model, Alibaba Qianwen and GPT3.5 to ensure the accuracy and efficiency of analysis.

[0048] In terms of database, this system supports multiple types such as mongodb and redis, which are used to store key information of job positions and key information of candidate resumes extracted after large model analysis. This design not only improves the flexibility and scalability of data storage, but also facilitates rapid matching during subsequent resume screening, greatly improving the efficiency of recruitment work.

[0049] Specifically, for HR recruiters, the resume screening system's human-computer interface is subdivided into the following functional modules: job position analysis module, which is used to deeply analyze and extract key requirements for job positions; candidate resume analysis module, which conducts a comprehensive analysis of uploaded candidate resumes and extracts key information; matching analysis module, which combines job requirements and candidate information to conduct accurate matching analysis and give a comprehensive ranking; and personal information module, which allows HR recruiters to manage their own personal information and account settings. See the core function diagram for details. Figure 2 , the specific implementation is as follows:

[0050] Job Analysis Module

[0051] HR recruitment specialists need to fill in the name, job overview, job description and rigid requirements of each position in this area. Multiple position information can be entered continuously, and the content is recommended to be as detailed as possible, but it is not mandatory. After each position's details are filled in, a data request should be sent through the preset API interface. At the same time, these detailed information needs to be merged with the specific prompt text and then sent to the big model together. The core function of this prompt is to guide the model to deeply analyze the job description entered by HR and accurately extract key information from it, covering educational background, work experience, professional skills, job responsibilities, required certificates, and soft skills. Subsequently, the system will store the extracted key information in the database in a unified format and in an orderly manner by position, so that it can be efficiently retrieved and called during subsequent matching analysis.

[0052] Candidate resume analysis module

[0053] HR recruitment specialists upload relevant resume information of candidates in this designated area, and support batch import of resume files in PDF or DOCX format. After sending the resume data request through the API, the system will receive and start a series of pre-processing procedures to clean, convert and standardize the data in the resume. The processed resume data will be merged with the specially designed prompt text and then passed to the big model together. The core function of this prompt is to guide the model to conduct an in-depth analysis of the resume content and accurately extract the key information of the candidate, including but not limited to name, contact information, educational background, work experience, professional skills, previous positions, certificates, personal soft skills and comprehensive evaluation. The extracted information will be formatted by the system according to a unified data structure, and then stored in the database in an orderly manner, which will facilitate subsequent matching analysis and rapid retrieval calls.

[0054] Matching analysis module

[0055] HR recruiters need to select a job to be recruited in the designated area and select some or all of the uploaded candidate resumes for matching. Once the match button is clicked, the system will extract formatted data sets related to the selected job and candidates from the database. These data will be merged with specific prompt text and transmitted to the big data analysis model together. The main function of the prompt text is to guide the model to conduct an in-depth comparative analysis of the detailed requirements of the job and the information of each candidate, and to perform linear scoring on each dimension according to the set standards.

[0056] Specifically, the scoring mechanism is as follows:

[0057] Educational background: The evaluation system places more emphasis on the relevance of the subject rather than the level of the degree. Candidates with a degree that is more closely related to the job position will receive a higher score, even if their degree level is relatively low.

[0058] Work experience: Emphasizes the candidate's work experience in related fields. If the candidate has more extensive and directly related work experience, his / her score will be increased accordingly.

[0059] Professional skills: Candidates are scored based on their relevant technical skills. Candidates with more proficient skills and higher professional level will receive higher scores.

[0060] Understanding of job responsibilities: This focuses on the scope and complexity of the job responsibilities that the candidate has undertaken in the past. Candidates who can demonstrate more job responsibilities related to the recruitment position will receive higher scores.

[0061] Credentials: A clear scoring criteria is adopted: candidates who hold the credentials clearly required for the position will receive high scores; if the candidate lacks the required credentials, no points will be given; and if they hold other credentials related to the position, they will receive a medium score.

[0062] Soft skills: Foreign language proficiency and leadership skills are valued. Candidates will score higher if they have more relevant soft skills, especially foreign language proficiency and leadership proficiency.

[0063] The scoring range for each dimension is set from 0 to 100. After completing the evaluation of each dimension, the big data analysis model will output the candidate's specific scores in terms of academic background, work experience, professional skills, understanding of job responsibilities, required certificates, and soft skills, and provide an overall evaluation.

[0064] The system will then weight each score according to the preset weights. By default, the weight of academic background is set to 0.1, the weight of work experience is set to 0.2, the weight of professional skills is set to 0.3, the weight of understanding of job responsibilities is set to 0.25, the weight of required certificates is set to 0.1, and the weight of soft skills is set to 0.05. The weighted scores are added together to get the candidate's final score.

[0065] Finally, the system will sort the candidates according to their final scores, and candidates with high scores will be displayed first on the page, thus helping HR recruitment specialists to screen out the most suitable candidates more efficiently.

[0066] Personal information management module

[0067] In this module, users can not only clearly check their unique personal identification ID, but also have the right to modify their passwords. In addition, to facilitate communication and contact between users and ensure timely contact in emergency situations, users can also update their contact information at any time, including but not limited to telephone numbers, email addresses, etc. At the same time, users can also make necessary modifications and improvements to their personal contact addresses based on actual conditions. It provides users with convenient and secure personal information management services, ensuring the accuracy and timeliness of user information.

[0068] This embodiment provides a large-model resume screening system for the human resources industry, including a job analysis module, a candidate resume analysis module, a matching analysis module, and a personal information module; the system relies on AI technology to intelligently perform in-depth analysis and rapid screening of a large number of resumes, thereby significantly shortening the screening cycle in the recruitment process and greatly improving work efficiency. This innovative solution effectively solves the bottleneck problems of long time and low efficiency that are commonly faced in the current recruitment process in various industries. The system can also quickly identify the candidates who best meet the job requirements based on job requirements through precise matching algorithms. This not only saves a lot of time and energy for recruiters, but also greatly improves the accuracy and success rate of recruitment. In general, this embodiment brings a new and efficient resume screening method to the recruitment industry by introducing cutting-edge technologies such as Inspur Yunhairuo large model, which helps recruiters quickly lock in outstanding talents in the fierce market competition.

[0069] Embodiment 2

[0070] Based on the first embodiment, the present invention provides a method for screening large model resumes in the human resources industry, comprising the following steps:

[0071] Through the job analysis module, we can deeply analyze and extract the key requirements of the job;

[0072] Through the candidate resume analysis module, the uploaded candidate resumes are comprehensively analyzed to extract key information;

[0073] The matching analysis module combines job requirements and candidate information to conduct accurate matching analysis and give a comprehensive ranking;

[0074] The personal information module allows users to manage their personal information and account settings.

[0075] Embodiment 3

[0076] Based on the second embodiment, this embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps in the large model resume screening method for the human resources industry as described in the first embodiment above are implemented.

[0077] Embodiment 4

[0078] Based on Example 3, this embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in a method for screening large model resumes in the human resources industry as described in Example 1 above are implemented.

[0079] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A large-scale resume screening system for the human resources industry, characterized by: The system consists of a job position analysis module, a candidate resume analysis module, a matching analysis module, and a personal information module; Job analysis module, used to deeply analyze and extract key requirements of jobs; Candidate resume analysis module, which conducts comprehensive analysis on uploaded candidate resumes and extracts key information; The matching analysis module combines job requirements and candidate information to conduct accurate matching analysis and give a comprehensive ranking; The personal information module allows users to manage their personal information and account settings.

2. According to claim 1, a large model resume screening system for the human resources industry is characterized by: The job position analysis module includes: PI interface, used to receive and send data requests; The large model analysis unit is used to deeply analyze the job description input by HR and accurately extract key information from it, covering educational background, work experience, professional skills, job responsibilities, required certificates and soft skills.

3. According to claim 1, a large model resume screening system for the human resources industry is characterized by: The candidate resume analysis module includes: Resume upload unit, supports batch import of resume files in PDF or DOCX format; Resume preprocessing unit, which cleans, converts and standardizes resume data; The large model analysis unit is used to conduct in-depth analysis of resume content and accurately extract key information of candidates, including but not limited to name, contact information, educational background, work experience, professional skills, previous positions, certificates held, personal soft skills and comprehensive evaluation.

4. According to claim 1, a large model resume screening system for the human resources industry is characterized by: The matching degree analysis module includes: A data extraction unit extracts formatted data sets related to selected positions and candidates from the database; Big data analysis model, which conducts in-depth comparative analysis of detailed job requirements and each candidate's information, and linearly scores each dimension according to the set standards; Scoring dimensions include academic background, work experience, professional skills, understanding of job responsibilities, certification status, and soft skills; The weighted processing unit performs weighted processing on each score according to the preset weight to obtain the candidate's final score; The sorting unit sorts the candidates according to their final scores, and the candidates with higher scores are displayed first.

5. The large-scale resume screening system for the human resources industry according to claim 1 is characterized by: The human information module includes: User identification ID display unit; Password modification unit; Contact update unit, supports updating of phone number, email address and other information; Contact address modification unit.

6. A method for screening large model resumes in the human resources industry, comprising the large model resume screening system for the human resources industry as described in any one of claims 1 to 5, characterized in that: The method comprises the following steps: Through the job analysis module, we can deeply analyze and extract the key requirements of the job; Through the candidate resume analysis module, the uploaded candidate resumes are comprehensively analyzed to extract key information; The matching analysis module combines job requirements and candidate information to conduct accurate matching analysis and give a comprehensive ranking; The personal information module allows users to manage their personal information and account settings.

7. The method for screening large model resumes in the human resources industry according to claim 6, characterized in that: The matching analysis module includes the following steps: Linearly score candidates on various dimensions according to the set standards; The scores of each item are weighted according to the preset weights to obtain the final score of the candidate; Candidates are sorted based on their final scores, with candidates with higher scores being displayed first.

8. The method for screening large model resumes in the human resources industry according to claim 7, characterized in that: Scoring dimensions include academic background, work experience, professional skills, understanding of job responsibilities, certification status and soft skills.

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