An organization member increasing method, device, equipment and medium

Abstract

The application belongs to the field of artificial intelligence, and relates to an organization recruitment method, which comprises the following steps: obtaining recruitment demand information of a target organization and target data of initial candidate objects; determining candidate objects from the initial candidate objects based on the recruitment demand information, the target data and a preset reference image; generating target interview questions of the candidate objects by using a preset question and answer model based on the target data of the candidate objects, so as to obtain reply data of the candidate objects to the target interview questions; scoring the reply data by using a preset scoring model, so as to obtain scoring results of the candidate objects; and selecting a to-be-recruited object from the candidate objects based on the scoring results. The application also provides an apparatus, a device and a medium. In addition, the application also relates to blockchain technology, and the recruitment demand information and the target data can be stored in the blockchain. The application can improve the recruitment efficiency of an organization.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, and in particular to a method, apparatus, device and medium for recruiting staff to an organization. Background Technology

[0002] In the insurance industry, the sustainable development and market competitiveness of insurance companies largely depend on the construction and expansion of their workforce, especially the recruitment of sales and service teams. In the traditional recruitment process of insurance companies, the recruitment interview stage plays a crucial role. However, this stage is generally plagued by numerous challenges and shortcomings.

[0003] Specifically, the traditional recruitment and interview process relies on manual resume screening, scheduling interviews, and face-to-face communication. These steps are not only time-consuming but also inefficient. Furthermore, factors such as differences in interviewers' subjective judgment and insufficient standardization of interview questions result in a lack of personalization and intelligence in the recruitment process, making it difficult to accurately match the talent needs of insurance companies for different positions. In conclusion, there is an urgent need for a method that can solve the problems of time-consuming and labor-intensive processes, lack of personalization and intelligence, and low recruitment efficiency inherent in the traditional recruitment and interview process. Summary of the Invention

[0004] The purpose of this application is to provide an organizational recruitment method, apparatus, equipment, and medium to solve the problems of time-consuming and labor-intensive recruitment and interview processes, lack of personalization and intelligence, and low recruitment efficiency.

[0005] To address the aforementioned technical problems, this application provides a method for increasing staff in an organization, employing the following technical solution:

[0006] Obtain recruitment needs information from target organizations and target data of initial candidates; based on recruitment needs information, target data of initial candidates, and preset reference profiles, identify candidates from the initial candidates; based on the target data of candidates, use a preset question-and-answer model to generate target interview questions for candidates, and obtain candidate response data to the target interview questions; use a preset scoring model to score the response data and obtain the candidate scoring results; based on the scoring results, select candidates to be recruited from the candidates.

[0007] Furthermore, before the step of determining candidate candidates from the initial candidate pool based on recruitment demand information, target data of the initial candidate pool, and preset reference profiles, the following steps are also included:

[0008] Acquire the target object's personal data; classify the target object according to a preset classification strategy and personal data to obtain classification results; use a preset feature extraction algorithm to extract the target object's feature information from the personal data; determine the feature weights of the feature information based on a preset weight allocation strategy; construct a profile framework for the target object based on the classification results, feature information, and feature weights; fill the profile framework with personal data to obtain a preset reference profile.

[0009] Furthermore, the steps for determining candidate candidates from the initial candidate pool based on recruitment demand information, target data of the initial candidate pool, and preset reference profiles specifically include:

[0010] Based on recruitment demand information and target data of initial candidates, the screening criteria are determined; target initial candidates that meet the screening criteria are selected from the initial candidates; feature vectors of target data of target initial candidates are extracted, and detailed profiles of target initial candidates are constructed based on feature vectors; similarity matching is performed between the detailed profile and the reference profile to obtain similarity matching results; based on the similarity matching results, target initial candidates are screened to obtain the screened candidates.

[0011] Furthermore, based on the target data of the candidate candidates, the steps of generating target interview questions for the candidate candidates using a pre-defined question-answering model specifically include:

[0012] Obtain job description information for the target positions applied for by candidate applicants; extract educational background and work experience information from the candidate applicants' target data; integrate the educational background, work experience, and job description information according to a preset data format to obtain model input data; analyze and process the model input data using a preset question-answering model to generate interview questions; and use a preset question quality assessment algorithm to filter the interview questions to obtain target interview questions.

[0013] Furthermore, the step of using a pre-defined scoring model to score the response data and obtain the scoring results for the candidate objects specifically includes:

[0014] Extract response features from the response data; input the response features into a preset scoring model to obtain preliminary scores for candidate objects; obtain preset scoring criteria, correct the preliminary scores according to the scoring criteria, and obtain the corrected target scores; output the target scores as the scoring results for candidate objects.

[0015] Furthermore, the step of selecting candidates for recruitment from the pool of candidates based on the scoring results specifically includes:

[0016] Obtain the reference score of the candidate; obtain the reference weight of the reference score and the target weight of the target score; perform a weighted average calculation on the reference score, reference weight, target score and target weight to obtain the weighted average score of the candidate; select candidates to be added from the candidate based on the weighted average score.

[0017] Furthermore, after the step of selecting candidates for recruitment based on the scoring results, the specific steps include:

[0018] Obtain historical employee data of the target organization; use a pre-set association rule mining algorithm to mine association rules between the attribute characteristics and career development paths of the target employees from the historical employee data; extract attribute feature vectors from the target data of the candidates to be recruited; determine the target career development path from the career development paths based on the association rules and attribute feature vectors; generate a career development reference for the candidates to be recruited based on the target career development path.

[0019] To address the aforementioned technical problems, this application also provides an organizational staffing extension device, which employs the following technical solution:

[0020] The acquisition module is used to acquire recruitment demand information from target organizations and target data of initial candidate objects;

[0021] The determination module is used to determine candidate objects from the initial candidate objects based on recruitment demand information, target data of initial candidate objects, and preset reference profiles;

[0022] The question generation module is used to generate target interview questions for candidate candidates based on the target data of the candidate candidates and using a preset question-and-answer model, so as to obtain the candidate candidates' response data to the target interview questions.

[0023] The scoring module is used to score the response data using a preset scoring model to obtain the scoring results of the candidate objects;

[0024] The selection module is used to select candidates for recruitment from the pool of candidates based on the scoring results.

[0025] To address the aforementioned technical problems, this application also provides a computer device, including a memory and a processor. The memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of the above-described organizational staffing method.

[0026] To address the aforementioned technical problems, embodiments of this application also provide a computer-readable storage medium storing computer-readable instructions that can be executed by at least one processor to cause the at least one processor to perform the steps of the above-described organizational recruitment method.

[0027] Compared with existing technologies, the embodiments of this application have the following main advantages: First, by acquiring the recruitment needs information of the target organization and the target data of the initial candidates, a precise grasp of recruitment needs and preliminary screening of candidates are achieved, effectively reducing the time cost of manual resume screening. Second, based on the preset reference profile, candidates who meet the job requirements can be quickly identified from the initial candidates. This process not only improves the accuracy of screening but also makes the recruitment process more personalized and intelligent. Third, by using a preset question-and-answer model to generate target interview questions and collecting the candidates' response data, this process not only achieves a combination of standardization and personalization of interview questions but also improves the relevance and efficiency of the interview. Finally, by scoring the response data using a preset scoring model, the performance of the candidates can be objectively and fairly evaluated, thereby ensuring that the selected candidates meet the actual needs of the target organization and improving the organization's recruitment efficiency. In summary, this technical solution, through intelligent and standardized processing methods, achieves comprehensive optimization of the recruitment and interview process, not only improving recruitment efficiency but also ensuring recruitment quality, enabling the target organization to conduct interviews and recruitment processes efficiently. Attached Figure Description

[0028] To more clearly illustrate the solutions in this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 This is an exemplary system architecture diagram to which this application can be applied;

[0030] Figure 2 This is a flowchart illustrating one method for recruiting staff in an organization, as provided in this application.

[0031] Figure 3 This is a schematic diagram of the structure of an organizational staffing device provided in this application;

[0032] Figure 4 This is a schematic diagram of the structure of a computer device provided in this application. Detailed Implementation

[0033] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein in the specification of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having," and any variations thereof, in the specification, claims, and foregoing drawings of this application, are intended to cover non-exclusive inclusion. The terms "first," "second," etc., in the specification, claims, or foregoing drawings of this application are used to distinguish different objects, not to describe a particular order.

[0034] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0035] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0036] like Figure 1 As shown, system architecture 100 may include terminal device 101, network 102, and server 103. Terminal device 101 may be a laptop 1011, tablet 1012, or mobile phone 1013. Network 102 is used as a medium to provide a communication link between terminal device 101 and server 103. Network 102 may include various connection types, such as wired, wireless communication links, or fiber optic cables.

[0037] Users can use terminal device 101 to interact with server 103 via network 102 to receive or send messages, etc. Various communication client applications can be installed on terminal device 101, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social media platform software, etc.

[0038] Terminal device 101 can be various electronic devices with a display screen and support web browsing. In addition to laptops 1011, tablets 1012, or mobile phones 1013, terminal device 101 can also be e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III), MP4 players (Moving Picture Experts Group Audio Layer IV), laptops, and desktop computers.

[0039] Server 103 can be a server that provides various services, such as a backend server that provides support for the pages displayed on terminal device 101.

[0040] It should be noted that the organizational staffing method provided in this application embodiment is generally executed by a server, and correspondingly, the organizational staffing device is generally set in the server.

[0041] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0042] Continue to refer to Figure 2 A flowchart of one embodiment of the organizational recruitment method according to this application is shown. The organizational recruitment method includes the following steps:

[0043] Step S201: Obtain the recruitment needs information of the target organization and the target data of the initial candidate objects.

[0044] In this embodiment, the organizational staffing method operates on electronic devices (e.g., Figure 1 The server shown can obtain recruitment request information via wired or wireless connection. It should be noted that the aforementioned wireless connection methods may include, but are not limited to, 3G / 4G / 5G connections, WiFi connections, Bluetooth connections, WiMAX connections, Zigbee connections, UWB (ultra wideband) connections, and other currently known or future known wireless connection methods.

[0045] In this context, the target organization refers to the organization that needs to recruit more employees. For example, if a large insurance company plans to expand its sales team, then the insurance company is the target organization.

[0046] The recruitment needs information refers to the specific requirements put forward by the target organization regarding new employees. This may include job type, number of openings, required skills, etc., and is used to guide the subsequent screening and matching process. For example, the recruitment needs information might include "Recruiting 5 sales managers with more than 3 years of insurance sales experience."

[0047] The initial candidate pool refers to the set of candidates who meet the basic criteria after initial screening. These candidates may come from various channels such as recruitment websites and internal referrals. For example, candidates from 100 resumes obtained from recruitment websites would be considered the initial candidate pool.

[0048] The target data for the initial candidates refers to the personal information provided by them, such as resumes and self-introductions. This information can be used for subsequent analysis and evaluation. Examples include the candidate's educational background, work experience, and professional skills.

[0049] Step S202: Based on the recruitment demand information, the target data of the initial candidate objects, and the preset reference profile, candidate objects are determined from the initial candidate objects.

[0050] The reference profile refers to an ideal employee image constructed based on the characteristics of outstanding employees of the target organization. It is used to match candidates. For example, the reference profile might include characteristics such as "5 years of insurance sales experience, excellent communication skills, and outstanding performance."

[0051] In this context, "candidates" refers to those who, after further screening, meet the recruitment requirements and closely match the reference profile. For example, 20 qualified candidates might be selected from 100 initial candidates.

[0052] Step S203: Based on the target data of the candidate, a preset question-and-answer model is used to generate the target interview questions for the candidate, so as to obtain the candidate's response data to the target interview questions.

[0053] The target data for candidates refers to more detailed information provided by candidates during the screening process, which is used to generate interview questions and evaluate responses. Examples include a candidate's specific work experience, project experience, and professional certifications.

[0054] Question-answering models, in this context, refer to machine learning-based algorithmic models used to generate interview questions based on target data about candidates. For example, a question-answering model can generate job-related professional questions based on a candidate's work experience.

[0055] Targeted interview questions refer to specific questions generated by the question-and-answer model based on the candidate's target data. These questions aim to assess the candidate's professional skills, work experience, and overall qualities. For example, "Please describe how you successfully completed a sales task in your past work."

[0056] The response data refers to the candidate's answers to the target interview questions, used to assess the candidate's abilities and suitability. For example, the candidate's description and explanation of the aforementioned sales task.

[0057] Step S204: Using a preset scoring model, the response data is scored to obtain the scoring results of the candidate objects.

[0058] In this context, a scoring model refers to a mathematical model that quantitatively evaluates candidate responses based on pre-defined algorithms and rules. This model extracts response features from the response data and uses these features to comprehensively score the candidate's response quality, professional competence, and adaptability. For example, a scoring model could be a neural network-based model that learns and extracts key features from responses by training on a large amount of historical interview data, thereby accurately scoring new response data.

[0059] The scoring result refers to the score obtained by evaluating the response data using a pre-defined scoring model, which is used to quantify the candidate's performance. For example, a score is given based on the quality of the candidate's answers and their match with the job requirements.

[0060] Step S205: Based on the scoring results, select candidates to be recruited from the candidate pool.

[0061] Among them, the candidates to be recruited refer to those who best meet the recruitment requirements based on the scoring results; they are the final recruitment targets. For example, five candidates with the highest scores are selected from 20 candidates.

[0062] This application embodiment achieves precise understanding of recruitment needs and preliminary screening of candidates by acquiring the target organization's recruitment needs information and initial candidate data, effectively reducing the time cost of manual resume screening. Secondly, based on a preset reference profile, it can quickly identify candidates who meet the job requirements from the initial pool of candidates. This process not only improves the accuracy of screening but also makes the recruitment process more personalized and intelligent. Furthermore, by using a preset question-and-answer model to generate target interview questions and collecting candidate responses, this process not only achieves a combination of standardization and personalization of interview questions but also improves the relevance and efficiency of the interviews. Finally, by scoring the response data using a preset scoring model, the performance of candidates can be objectively and fairly evaluated, ensuring that the selected candidates meet the actual needs of the target organization and improving the organization's recruitment efficiency. In summary, this technical solution, through intelligent and standardized processing methods, achieves comprehensive optimization of the recruitment and interview process, not only improving recruitment efficiency but also ensuring recruitment quality, enabling target organizations to conduct interviews and recruitment processes efficiently.

[0063] In some optional implementations of this embodiment, before determining candidate objects from the initial candidate objects in step 202 based on recruitment demand information, target data of initial candidate objects, and preset reference profiles, the following steps are further included:

[0064] Acquire the target object's personal data; classify the target object according to a preset classification strategy and personal data to obtain classification results; use a preset feature extraction algorithm to extract the target object's feature information from the personal data; determine the feature weights of the feature information based on a preset weight allocation strategy; construct a profile framework for the target object based on the classification results, feature information, and feature weights; fill the profile framework with personal data to obtain a preset reference profile.

[0065] The target group refers to the employees within the target organization. By acquiring the personal data of these employees, a reference profile is constructed.

[0066] Personal data refers to personal information provided by the target individual, including but not limited to educational background, work experience, and skill level. This information can be used to build profiles and conduct assessments.

[0067] Classification strategies refer to methods of categorizing target objects according to preset standards and rules. For example, target objects can be categorized into "junior," "intermediate," and "senior" based on their years of work experience.

[0068] The classification result refers to the outcome obtained after classifying the target object according to the classification strategy. For example, a candidate (target object) is classified as a "mid-level" salesperson.

[0069] Feature extraction algorithms, in particular, are algorithms that extract information related to the characteristics of a target individual from personal data. For example, extracting key information related to sales targets from the target individual's work experience.

[0070] In this context, the feature information of the target object refers to the information obtained through feature extraction algorithms that describes the characteristics of the target object. Examples include a candidate's sales performance and communication skills.

[0071] Weighting strategies refer to methods that assign different weights to features based on their importance and relevance. For example, for sales personnel, sales performance and communication skills might be given higher weights.

[0072] Feature weights refer to the weight values ​​assigned to feature information according to a weighting strategy. For example, sales performance has a weight of 0.5, and communication skills have a weight of 0.3.

[0073] In this context, a profile framework refers to a model framework built based on classification results and feature weights to describe the characteristics of a target object. For example, a profile framework that includes features such as sales performance, communication skills, and work experience.

[0074] In one example, firstly, personal information of the target object can be collected through legal means, including but not limited to age, gender, occupation, hobbies, and consumption habits. Next, based on a pre-defined classification strategy (such as classification based on occupation or age group), the target object is classified using the personal data to obtain the classification results. Then, a pre-defined feature extraction algorithm (such as decision trees or random forests in machine learning algorithms) is used to extract feature information of the target object from the personal data, such as consumption preferences and social activity. Then, based on a pre-defined weight allocation strategy (such as based on the importance of feature information and its relevance to the target object's profile), the feature weights of each feature are determined. Finally, combining the classification results, feature information, and feature weights, a profile framework for the target object is constructed. Finally, based on the personal data, the profile framework is populated to obtain a pre-defined reference profile. This reference profile can comprehensively and accurately reflect the characteristics of the target object.

[0075] This application embodiment achieves the construction of an accurate profile of a target object by systematically processing its personal data. First, a pre-defined classification strategy ensures that the target object can be reasonably categorized, providing a basic framework for subsequent profile construction. Next, a pre-defined feature extraction algorithm is used to deeply mine key information in the personal data; this feature information comprehensively reflects the characteristics of the target object. A weight allocation strategy further improves the accuracy of profile construction by assigning appropriate weights to different feature information, ensuring the rationality and importance of each feature in the profile. Finally, the profile framework constructed by combining the classification results, feature information, and feature weights, and then populated with personal data, yields a pre-defined reference profile. This reference profile is not only comprehensive and accurate but also flexibly applicable to different scenarios.

[0076] In some optional implementations of this embodiment, step S202, determining candidate objects from the initial candidate objects based on recruitment demand information, target data of initial candidate objects, and preset reference profiles, specifically includes the following steps:

[0077] Based on recruitment demand information and target data of initial candidates, the screening criteria are determined; target initial candidates that meet the screening criteria are selected from the initial candidates; feature vectors of target data of target initial candidates are extracted, and detailed profiles of target initial candidates are constructed based on feature vectors; similarity matching is performed between the detailed profile and the reference profile to obtain similarity matching results; based on the similarity matching results, target initial candidates are screened to obtain the screened candidates.

[0078] The screening criteria refer to the conditions set for selecting candidates based on recruitment needs and reference profiles. For example, candidates may be required to have more than 3 years of sales experience and excellent performance.

[0079] Among them, the feature vector refers to the transformation of the target data of the initial candidate object into a vector form for subsequent analysis and calculation.

[0080] In this context, a detailed profile refers to a profile built based on the candidate's target data and feature vectors, containing more detailed information. For example, a detailed profile includes information such as the candidate's specific work experience, project experience, and professional skills.

[0081] In one example, the target organization needs to recruit a backend development engineer with Java development experience. First, recruitment requirements information, such as skill requirements (Java, Spring Boot) and work experience (3+ years), can be extracted from the job description. Then, target data for initial candidates is obtained from a resume database, including educational background, work experience, and skill certifications. Next, screening criteria are set, such as a bachelor's degree or above and Java project experience, to select qualified initial candidates. Subsequently, machine learning algorithms are used to extract feature vectors from the target data, constructing a detailed profile, including dimensions such as technical skills, project experience, and communication skills. Simultaneously, a reference profile is constructed based on historical data of outstanding employees. The similarity between the detailed profile and the reference profile is calculated using cosine similarity. Candidates with similarity scores above a threshold are selected for further interviews, significantly improving the accuracy and efficiency of recruitment.

[0082] This application embodiment achieves preliminary and accurate screening of candidates by carefully setting screening conditions by combining recruitment demand information with target data of initial candidates. Based on this, the target data feature vectors of the initial candidates are further extracted, and detailed profiles are constructed accordingly. This step greatly enriches the personalized information of the candidates, providing a solid foundation for subsequent matching. By performing similarity matching between the detailed profiles and reference profiles, this solution can quantitatively assess the degree of fit between candidates and job requirements, ensuring the objectivity and accuracy of the screening process. Ultimately, screening based on similarity matching results can efficiently identify candidates who are highly matched to job requirements, not only improving recruitment efficiency but also effectively reducing the risk of mismatch, bringing significant optimization effects to the target organization's human resource management.

[0083] In some optional implementations of this embodiment, step S203, based on the target data of the candidate objects, uses a preset question-and-answer model to generate target interview questions for the candidate objects, specifically includes the following steps:

[0084] Obtain job description information for the target positions applied for by candidate applicants; extract educational background and work experience information from the candidate applicants' target data; integrate the educational background, work experience, and job description information according to a preset data format to obtain model input data; analyze and process the model input data using a preset question-answering model to generate interview questions; and use a preset question quality assessment algorithm to filter the interview questions to obtain target interview questions.

[0085] Job description information refers to a detailed description of the target position, including job responsibilities and requirements. For example, the job description for a sales manager might include "responsible for developing sales strategies and achieving sales targets."

[0086] In this context, data format refers to integrating educational background information, work experience information, and job description information into a format that the question-answering model can recognize. For example, integrating information into JSON or XML format.

[0087] The question quality assessment algorithm is used to evaluate whether the generated interview questions meet preset standards. For example, it assesses whether the questions are relevant to the job description information and whether they help evaluate the candidate's abilities.

[0088] In one example, the target organization needs to recruit a software development engineer. The job description includes programming language requirements (e.g., Java, Python), project development experience, teamwork skills, etc. The target data for candidates includes their educational background (e.g., computer science major, master's degree), work experience (e.g., participation in multiple large-scale software development projects, holding a core development role), etc. The job description, educational background, and work experience information can be integrated according to a preset data format (e.g., JSON) to form the model input data. A pre-trained question-answering model (e.g., BERT model) is then used to analyze and process the model input data, generating interview questions highly relevant to the job description and candidate background, such as "Please describe the biggest challenge you encountered in Java project development and how you solved it," and "How to effectively coordinate project progress and team members' work within a team." A preset question quality assessment algorithm (e.g., semantic similarity calculation based on natural language processing technology, question complexity assessment, etc.) is used to filter the interview questions, removing duplicate, ambiguous, and overly simple questions to obtain the target interview questions.

[0089] This application embodiment utilizes intelligent methods to achieve comprehensive analysis of candidate applications for target positions. By accurately extracting candidates' educational background and work experience information and integrating it with job description information, structured model input data is formed, providing a solid foundation for subsequent analysis. A pre-set question-answering model is used to perform in-depth analysis of the model input data, automatically generating interview questions highly relevant to the candidate's background and job requirements, ensuring the questions' relevance and specificity. Simultaneously, a pre-set question quality assessment algorithm filters interview questions, effectively removing repetitive, vague, and low-quality questions, further improving the quality and effectiveness of the interview questions. This technical solution not only improves the efficiency of interview question generation but also reduces the subjectivity and uncertainty of manually designing questions, contributing to improved accuracy and efficiency in recruitment.

[0090] In some optional implementations of this embodiment, step S204, which uses a preset scoring model to score the response data and obtain the scoring results of the candidate objects, specifically includes the following steps:

[0091] Extract response features from the response data; input the response features into a preset scoring model to obtain preliminary scores for candidate objects; obtain preset scoring criteria, correct the preliminary scores according to the scoring criteria, and obtain the corrected target scores; output the target scores as the scoring results for candidate objects.

[0092] Response features refer to key information relevant to the evaluation extracted from the response data. For example, descriptions of sales strategies and objectives extracted from a candidate's responses.

[0093] The preliminary score refers to the score obtained by using a scoring model to initially assess the characteristics of the response. For example, a preliminary score is given based on the quality of the candidate's response and its match with the job requirements.

[0094] The scoring criteria serve as a reference standard to correct initial scores and ensure that the scoring results are objective and fair. For example, scoring criteria may be developed based on historical data and industry standards.

[0095] The target score refers to the final score result after correction according to the scoring criteria.

[0096] In one example, key features can be extracted from candidate interview responses, such as fluency of language expression, accuracy of professional knowledge, logicality of answers, and stability of emotional control. These features can be quantified using natural language processing techniques (such as text segmentation, sentiment analysis, and keyword extraction). The extracted response features are then input into a pre-defined scoring model. This model can be built based on machine learning algorithms (such as support vector machines, random forests, and neural networks), trained on a large amount of historical interview data, enabling it to automatically provide preliminary scores based on the response features. Pre-defined scoring criteria are then obtained, which can be customized according to different recruitment needs and job characteristics, including the weighting of various response features, scoring ranges, and scoring levels. Based on the scoring criteria, the preliminary scores are corrected to eliminate potential biases in the scoring model, resulting in a more objective and accurate corrected target score.

[0097] This application's embodiments significantly improve the accuracy and objectivity of candidate evaluation through a refined data processing workflow. First, the extraction of response features accurately captures key information from the response data, providing strong support for subsequent evaluation. Second, the application of a pre-set evaluation model automates the generation of preliminary scores, improving evaluation efficiency. Furthermore, the preliminary scores are corrected using pre-set evaluation criteria, ensuring fairness and consistency and effectively avoiding the subjectivity and uncertainty of human evaluation. Finally, the output target score serves as the evaluation result for the candidate, reflecting not only the candidate's true level but also providing a reliable basis for subsequent decision-making.

[0098] In some optional implementations of this embodiment, step S205, selecting candidates for recruitment based on the scoring results, may specifically include the following steps:

[0099] Obtain the reference score of the candidate; obtain the reference weight of the reference score and the target weight of the target score; perform a weighted average calculation on the reference score, reference weight, target score and target weight to obtain the weighted average score of the candidate; select candidates to be added from the candidate based on the weighted average score.

[0100] The reference score refers to the interview scores of the candidate obtained from other interview terminals. For example, scores given by multiple interviewers who evaluate the candidate and submit them through the interview terminal.

[0101] The reference weight refers to the weight value assigned to the reference score, which is used to take its influence into account in the weighted calculation. For example, the weight assigned to the reference score based on its importance and relevance.

[0102] Here, target weight refers to the weight value assigned to the target score, used to take its impact into account in the weighted calculation. For example, the weight assigned to the target score based on the importance and relevance of the current assessment.

[0103] The weighted average score refers to the average score obtained by weighting the reference score, reference weight, target score, and target weight.

[0104] In one example, preliminary scores can be given by the human resources department or recruitment experts based on candidates' resumes, educational backgrounds, work experience, and other basic information. A weight (e.g., 0.4) is assigned to the reference scores based on the target organization's recruitment needs and job characteristics. A pre-defined scoring model is used to score the responses, yielding the target scores for each candidate. Similarly, a weight (e.g., 0.6) is assigned to the target scores based on the target organization's recruitment needs and job characteristics. The reference and target scores for each candidate are multiplied by their respective weights to obtain weighted reference and target scores. The weighted reference and target scores are then summed to obtain the weighted average score for each candidate. Candidates are ranked based on their weighted average scores. Candidates with higher weighted average scores are selected as candidates for further recruitment and proceed to the subsequent hiring process.

[0105] This application's embodiments achieve a comprehensive and accurate quantification of candidate capabilities by introducing a two-dimensional evaluation system of reference and target scores, and cleverly integrating reference and target weights. Based on this quantified scoring result, candidates can be selected more scientifically and fairly, effectively improving the accuracy and efficiency of talent selection.

[0106] In some optional implementations of this embodiment, after selecting candidates for recruitment based on the scoring results in step S205, the following steps may also be included:

[0107] Obtain historical employee data of the target organization; use a pre-set association rule mining algorithm to mine association rules between the attribute characteristics and career development paths of the target employees from the historical employee data; extract attribute feature vectors from the target data of the candidates to be recruited; determine the target career development path from the career development paths based on the association rules and attribute feature vectors; generate a career development reference for the candidates to be recruited based on the target career development path.

[0108] Historical employee data refers to the personal information and work performance data of past employees of the target organization, used to mine association rules and career development paths. For example, employee data and performance records of a sales team of an insurance company over the past 5 years.

[0109] Association rule mining algorithms refer to algorithms that extract relationships between variables from large amounts of data. For example, mining association rules between target employee attributes and career development paths from historical employee data.

[0110] Among them, attribute features refer to attribute information that describes the characteristics of the target employee, such as age, education, and work experience. For example, the age and education of the target employee are attribute features.

[0111] Association rules, in particular, are rules that describe the relationships between variables, obtained through association rule mining algorithms. For example, "Employees with more than 5 years of sales experience and excellent performance are more likely to be promoted to sales manager" is an association rule.

[0112] In this context, attribute feature vectors refer to the transformation of employee attributes into vector form for subsequent analysis and calculation. For example, an employee's age, education level, work experience, and other attributes can be converted into a vector containing multiple numerical values.

[0113] The target career development path refers to the career development path determined for potential recruits based on association rules and attribute feature vectors. For example, a career development path from sales consultant to sales manager determined for potential recruits.

[0114] Among them, career development reference refers to career development suggestions and plans provided to potential recruits based on their target career development path.

[0115] In one example, historical employee data can be exported from the target organization's human resources information system. This data includes basic employee information (such as gender, age, education, and major), work performance (such as performance evaluations and project participation), and promotion records. Next, the Apriori algorithm is used as the pre-defined association rule mining algorithm to process the historical employee data and uncover association rules between employee attributes and career development paths. For example, it is found that employees who graduated from "985 / 211 universities with relevant majors" are more likely to receive promotions within three years of joining the company. Following this, basic information and work performance of potential hires (such as newly hired graduates) are collected, and their attribute feature vectors, such as education, major, and onboarding performance, are extracted. Then, the attribute feature vectors of potential hires are matched with the mined association rules to calculate the career development path that best matches their attribute characteristics—the target career development path. Finally, based on the target career development path and in conjunction with the company's internal job structure and promotion mechanism, personalized career development references are generated for prospective recruits, including short-term goals (such as improving professional skills and participating in specific projects), medium-term goals (such as obtaining promotion opportunities and taking on more responsibilities), and long-term goals (such as becoming a department head or industry expert).

[0116] This application's embodiments, through in-depth mining of historical employee data from target organizations and the application of advanced association rule mining algorithms, reveal the intrinsic relationship between employee attribute characteristics and career development paths. By accurately extracting the attribute feature vectors of potential hires and matching them with the mined association rules, their target career development paths can be predicted and determined in a personalized manner. This innovative method not only provides potential hires with a scientific and reasonable career development blueprint but also significantly enhances the pertinence and effectiveness of career development planning. With this career development reference, potential hires can more clearly understand their own development path, clarify their career goals, and thus effectively improve their personal professional qualities and accelerate their personal growth. Simultaneously, this technical solution also provides strong support for the talent cultivation and career development management of target organizations, contributing to the construction of a more comprehensive and efficient human resource management system.

[0117] It should be emphasized that, in order to further ensure the privacy and security of the aforementioned recruitment demand information and target data, the aforementioned recruitment demand information and target data can also be stored in a blockchain node.

[0118] The blockchain referred to in this application is a novel application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms. Essentially, a blockchain is a decentralized database, a chain of data blocks linked together using cryptographic methods. Each data block contains information about a batch of network transactions, used to verify the validity of the information (anti-counterfeiting) and generate the next block. A blockchain can include an underlying blockchain platform, a platform product service layer, and an application service layer.

[0119] The embodiments of this application can construct and optimize related models and networks based on artificial intelligence technology, such as question-answering models and scoring models. Artificial intelligence (AI) models are the culmination of theory and practice in simulating human intelligent decision-making processes through algorithms and data analysis to solve complex problems, predict future trends, or automate tasks. These models utilize large amounts of historical data and real-time information, trained and optimized through specific algorithmic frameworks to achieve efficient, accurate, and reliable performance.

[0120] 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 instructing related hardware with computer-readable instructions. These computer-readable instructions can be stored in a computer-readable storage medium. When executed, the program can include the processes of the embodiments of the above methods. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, optical disk, or read-only memory (ROM), or random access memory (RAM).

[0121] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by 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 flowcharts of the accompanying figures 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, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0122] Further reference Figure 3 As a response to the above Figure 2 The implementation of the method shown in this application provides an embodiment of an organizational staffing device, which is similar to... Figure 2Corresponding to the method embodiments shown, this device can be specifically applied to various electronic devices.

[0123] like Figure 3 As shown, the organizational staffing device 400 in this embodiment includes: an acquisition module 401, a determination module 402, a question generation module 403, a scoring module 404, and a selection module 405.

[0124] in:

[0125] The acquisition module 401 is used to acquire the recruitment needs information of the target organization and the target data of the initial candidate objects;

[0126] The determination module 402 is used to determine candidate objects from the initial candidate objects based on recruitment demand information, target data of initial candidate objects, and preset reference profiles;

[0127] The question generation module 403 is used to generate target interview questions for candidate candidates based on the target data of the candidate candidates and using a preset question-and-answer model, so as to obtain the candidate candidates' response data to the target interview questions.

[0128] The scoring module 404 is used to score the response data using a preset scoring model to obtain the scoring results of the candidate objects;

[0129] Selection module 405 is used to select candidates for recruitment from the candidate pool based on the scoring results.

[0130] This application embodiment achieves precise understanding of recruitment needs and preliminary screening of candidates by acquiring the target organization's recruitment needs information and initial candidate data, effectively reducing the time cost of manual resume screening. Secondly, based on a preset reference profile, it can quickly identify candidates who meet the job requirements from the initial pool of candidates. This process not only improves the accuracy of screening but also makes the recruitment process more personalized and intelligent. Furthermore, by using a preset question-and-answer model to generate target interview questions and collecting candidate responses, this process not only achieves a combination of standardization and personalization of interview questions but also improves the relevance and efficiency of the interviews. Finally, by scoring the response data using a preset scoring model, the performance of candidates can be objectively and fairly evaluated, ensuring that the selected candidates meet the actual needs of the target organization and improving the organization's recruitment efficiency. In summary, this technical solution, through intelligent and standardized processing methods, achieves comprehensive optimization of the recruitment and interview process, not only improving recruitment efficiency but also ensuring recruitment quality, enabling target organizations to conduct interviews and recruitment processes efficiently.

[0131] In one embodiment, the determining module 402 includes:

[0132] The condition determination submodule is used to determine the screening conditions based on the recruitment demand information and the target data of the initial candidate objects;

[0133] The first filtering submodule is used to filter out target initial candidate objects that meet the filtering criteria from the initial candidate objects;

[0134] The construction submodule is used to extract the feature vectors of the target data of the initial candidate objects, and to construct a detailed profile of the initial candidate objects based on the feature vectors.

[0135] The matching submodule is used to perform similarity matching between the detailed profile and the reference profile to obtain the similarity matching results;

[0136] The second filtering submodule is used to filter the initial candidate objects based on the similarity matching results to obtain the filtered candidate objects.

[0137] This application embodiment achieves preliminary and accurate screening of candidates by carefully setting screening conditions by combining recruitment demand information with target data of initial candidates. Based on this, the target data feature vectors of the initial candidates are further extracted, and detailed profiles are constructed accordingly. This step greatly enriches the personalized information of the candidates, providing a solid foundation for subsequent matching. By performing similarity matching between the detailed profiles and reference profiles, this solution can quantitatively assess the degree of fit between candidates and job requirements, ensuring the objectivity and accuracy of the screening process. Ultimately, screening based on similarity matching results can efficiently identify candidates who are highly matched to job requirements, not only improving recruitment efficiency but also effectively reducing the risk of mismatch, bringing significant optimization effects to the target organization's human resource management.

[0138] In one embodiment, the problem generation module 403 includes:

[0139] The description information acquisition submodule is used to obtain the job description information of the target position applied for by the candidate;

[0140] The information extraction submodule is used to extract educational background and work experience information from the target data of candidate objects;

[0141] The integration submodule is used to integrate educational background information, work experience information, and job description information according to a preset data format to obtain the model input data;

[0142] The analysis submodule is used to analyze and process the input data of the pre-set question-and-answer model to generate interview questions;

[0143] The third filtering submodule is used to filter interview questions using a preset question quality assessment algorithm to obtain target interview questions.

[0144] This application embodiment utilizes intelligent methods to achieve comprehensive analysis of candidate applications for target positions. By accurately extracting candidates' educational background and work experience information and integrating it with job description information, structured model input data is formed, providing a solid foundation for subsequent analysis. A pre-set question-answering model is used to perform in-depth analysis of the model input data, automatically generating interview questions highly relevant to the candidate's background and job requirements, ensuring the questions' relevance and specificity. Simultaneously, a pre-set question quality assessment algorithm filters interview questions, effectively removing repetitive, vague, and low-quality questions, further improving the quality and effectiveness of the interview questions. This technical solution not only improves the efficiency of interview question generation but also reduces the subjectivity and uncertainty of manually designing questions, contributing to improved accuracy and efficiency in recruitment.

[0145] In one embodiment, the scoring module 404 includes:

[0146] The feature extraction submodule is used to extract response features from the response data;

[0147] The input submodule is used to input the response features into a preset scoring model to obtain a preliminary score for the candidate object;

[0148] The standard acquisition submodule is used to acquire preset scoring standards, correct the preliminary scores according to the scoring standards, and obtain the corrected target scores; the target scores are then output as the scoring results of the candidate objects.

[0149] This application's embodiments significantly improve the accuracy and objectivity of candidate evaluation through a refined data processing workflow. First, the extraction of response features accurately captures key information from the response data, providing strong support for subsequent evaluation. Second, the application of a pre-set evaluation model automates the generation of preliminary scores, improving evaluation efficiency. Furthermore, the preliminary scores are corrected using pre-set evaluation criteria, ensuring fairness and consistency and effectively avoiding the subjectivity and uncertainty of human evaluation. Finally, the output target score serves as the evaluation result for the candidate, reflecting not only the candidate's true level but also providing a reliable basis for subsequent decision-making.

[0150] In one embodiment, the selection module 405 includes:

[0151] The score acquisition submodule is used to obtain reference scores for candidate objects;

[0152] The weight acquisition submodule is used to obtain the reference weight of the reference score and the target weight of the target score.

[0153] The weighting submodule is used to perform weighted calculations on the reference score, reference weight, target score, and target weight to obtain the weighted average score of the candidate.

[0154] The selection submodule is used to select candidates for recruitment from among the candidates based on a weighted average score.

[0155] This application's embodiments achieve a comprehensive and accurate quantification of candidate capabilities by introducing a two-dimensional evaluation system of reference and target scores, and cleverly integrating reference and target weights. Based on this quantified scoring result, candidates can be selected more scientifically and fairly, effectively improving the accuracy and efficiency of talent selection.

[0156] In one embodiment, the staffing extension device 400 further includes:

[0157] The data acquisition module is used to acquire the personal data of the target object;

[0158] The classification module is used to classify target objects according to preset classification strategies and personal data, and obtain classification results;

[0159] The information extraction module is used to extract feature information of target objects from personal data using a preset feature extraction algorithm;

[0160] The weight determination module is used to determine the feature weights of feature information based on a preset weight allocation strategy.

[0161] The building module is used to construct a profile framework for the target object based on the classification results, feature information, and feature weights.

[0162] The fill module is used to fill in the profile frame based on personal data to obtain a preset reference profile.

[0163] This application embodiment achieves the construction of an accurate profile of a target object by systematically processing its personal data. First, a pre-defined classification strategy ensures that the target object can be reasonably categorized, providing a basic framework for subsequent profile construction. Next, a pre-defined feature extraction algorithm is used to deeply mine key information in the personal data; this feature information comprehensively reflects the characteristics of the target object. A weight allocation strategy further improves the accuracy of profile construction by assigning appropriate weights to different feature information, ensuring the rationality and importance of each feature in the profile. Finally, the profile framework constructed by combining the classification results, feature information, and feature weights, and then populated with personal data, yields a pre-defined reference profile. This reference profile is not only comprehensive and accurate but also flexibly applicable to different scenarios.

[0164] In one embodiment, the staffing extension device 400 may further include:

[0165] The employee data acquisition module is used to acquire historical employee data of the target organization;

[0166] The data mining module is used to extract association rules between the attribute characteristics and career development paths of target employees from historical employee data using a preset association rule mining algorithm.

[0167] The vector extraction module is used to extract the attribute feature vectors of the target data of the object to be recruited;

[0168] The path determination module is used to determine the target career development path from career development paths based on association rules and attribute feature vectors;

[0169] The reference generation module is used to generate career development references for candidates to be recruited, based on their target career development paths.

[0170] This application's embodiments, through in-depth mining of historical employee data from target organizations and the application of advanced association rule mining algorithms, reveal the intrinsic relationship between employee attribute characteristics and career development paths. By accurately extracting the attribute feature vectors of potential hires and matching them with the mined association rules, their target career development paths can be predicted and determined in a personalized manner. This innovative method not only provides potential hires with a scientific and reasonable career development blueprint but also significantly enhances the pertinence and effectiveness of career development planning. With this career development reference, potential hires can more clearly understand their own development path, clarify their career goals, and thus effectively improve their personal professional qualities and accelerate their personal growth. Simultaneously, this technical solution also provides strong support for the talent cultivation and career development management of target organizations, contributing to the construction of a more comprehensive and efficient human resource management system.

[0171] To address the aforementioned technical problems, embodiments of this application also provide a computer device. Please refer to [link / reference needed] for details. Figure 4 , Figure 4 This is a basic structural block diagram of the computer device in this embodiment.

[0172] Computer device 6 includes a memory 61, a processor 62, and a network interface 63 that are interconnected via a system bus. It should be noted that only computer device 6 with memory 61, processor 62, and network interface 63 is shown in the figure; however, it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented alternatively. Those skilled in the art will understand that the computer device described herein is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0173] Computer devices can include desktop computers, laptops, handheld computers, and cloud servers. These devices allow for human-computer interaction with users through keyboards, mice, remote controls, touchpads, or voice-activated devices.

[0174] The memory 61 includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 61 may be an internal storage unit of the computer device 6, such as the hard disk or memory of the computer device 6. In other embodiments, the memory 61 may also be an external storage device of the computer device 6, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 6. Of course, the memory 61 may include both the internal storage unit and the external storage device of the computer device 6. In this embodiment, the memory 61 is typically used to store the operating system and various application software installed on the computer device 6, such as computer-readable instructions for organizational recruitment methods. In addition, the memory 61 may also be used to temporarily store various types of data that have been output or will be output.

[0175] In some embodiments, processor 62 may be a central processing unit (CPU), controller, microcontroller, microprocessor, or other data processing chip. This processor 62 is typically used to control the overall operation of the computer device 6. In this embodiment, processor 62 is used to execute computer-readable instructions stored in memory 61 or to process data, such as computer-readable instructions for executing a personnel addition method.

[0176] The network interface 63 may include a wireless network interface or a wired network interface, which is typically used to establish a communication connection between the computer device 6 and other electronic devices.

[0177] This application embodiment achieves precise understanding of recruitment needs and preliminary screening of candidates by acquiring the target organization's recruitment needs information and initial candidate data, effectively reducing the time cost of manual resume screening. Secondly, based on a preset reference profile, it can quickly identify candidates who meet the job requirements from the initial pool of candidates. This process not only improves the accuracy of screening but also makes the recruitment process more personalized and intelligent. Furthermore, by using a preset question-and-answer model to generate target interview questions and collecting candidate responses, this process not only achieves a combination of standardization and personalization of interview questions but also improves the relevance and efficiency of the interviews. Finally, by scoring the response data using a preset scoring model, the performance of candidates can be objectively and fairly evaluated, ensuring that the selected candidates meet the actual needs of the target organization and improving the organization's recruitment efficiency. In summary, this technical solution, through intelligent and standardized processing methods, achieves comprehensive optimization of the recruitment and interview process, not only improving recruitment efficiency but also ensuring recruitment quality, enabling target organizations to conduct interviews and recruitment processes efficiently.

[0178] This application also provides another embodiment, namely, providing a computer-readable storage medium storing computer-readable instructions that can be executed by at least one processor to cause the at least one processor to perform the steps of the organization recruitment method described above.

[0179] This application embodiment achieves precise understanding of recruitment needs and preliminary screening of candidates by acquiring the target organization's recruitment needs information and initial candidate data, effectively reducing the time cost of manual resume screening. Secondly, based on a preset reference profile, it can quickly identify candidates who meet the job requirements from the initial pool of candidates. This process not only improves the accuracy of screening but also makes the recruitment process more personalized and intelligent. Furthermore, by using a preset question-and-answer model to generate target interview questions and collecting candidate responses, this process not only achieves a combination of standardization and personalization of interview questions but also improves the relevance and efficiency of the interviews. Finally, by scoring the response data using a preset scoring model, the performance of candidates can be objectively and fairly evaluated, ensuring that the selected candidates meet the actual needs of the target organization and improving the organization's recruitment efficiency. In summary, this technical solution, through intelligent and standardized processing methods, achieves comprehensive optimization of the recruitment and interview process, not only improving recruitment efficiency but also ensuring recruitment quality, enabling target organizations to conduct interviews and recruitment processes efficiently.

[0180] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods of the various embodiments of this application.

[0181] Obviously, the embodiments described above are only some embodiments of this application, not all embodiments. The accompanying drawings show preferred embodiments of this application, but do not limit the patent scope of this application. This application can be implemented in many different forms; rather, the purpose of providing these embodiments is to provide a more thorough and comprehensive understanding of the disclosure of this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this application's specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the scope of patent protection of this application.

Claims

1. A method for increasing staff in an organization, characterized in that, Includes the following steps: Obtain recruitment needs information from target organizations and target data for initial candidates; Based on the recruitment demand information, the target data of the initial candidate objects, and the preset reference profile, candidate objects are determined from the initial candidate objects; Based on the target data of the candidate, a preset question-and-answer model is used to generate target interview questions for the candidate, so as to obtain the candidate's response data to the target interview questions; The response data is scored using a preset scoring model to obtain the scoring results of the candidate object; Based on the scoring results, candidates to be recruited are selected from the candidate pool. Obtain historical employee data of the target organization; A preset association rule mining algorithm is used to mine association rules between the attribute characteristics and career development paths of target employees from the historical employee data; Extract the attribute feature vector of the target data of the object to be recruited; Based on the association rules and the attribute feature vectors, a target career development path is determined from the career development paths; Based on the target career development path, a career development reference is generated for the candidates to be recruited.

2. The method according to claim 1, characterized in that, Before the step of determining candidate objects from the initial candidate objects based on the recruitment demand information, the target data of the initial candidate objects, and the preset reference profile, the method further includes: Obtain the target's personal data; Based on a preset classification strategy and the personal data, the target object is classified to obtain a classification result; A preset feature extraction algorithm is used to extract feature information of the target object from the personal data; Based on a preset weight allocation strategy, the feature weights of the feature information are determined; Based on the classification results, the feature information, and the feature weights, a profile framework for the target object is constructed. Based on the personal data, the portrait frame is filled in to obtain a preset reference portrait.

3. The method according to claim 2, characterized in that, The step of determining candidate objects from the initial candidate objects based on the recruitment demand information, the target data of the initial candidate objects, and the preset reference profile specifically includes: Based on the recruitment demand information and the target data of the initial candidate objects, the screening criteria are determined; Select target initial candidate objects that meet the selection criteria from the initial candidate objects; Extract the feature vector of the target data of the initial candidate target, and construct a detailed profile of the initial candidate target based on the feature vector; The detailed portrait and the reference portrait are matched for similarity to obtain similarity matching results; Based on the similarity matching results, the initial target candidate objects are filtered to obtain the filtered candidate objects.

4. The method according to claim 1, characterized in that, The step of generating target interview questions for the candidate candidates based on their target data and using a preset question-answering model specifically includes: Obtain the job description information of the target position applied for by the candidate; Educational background information and work experience information are extracted from the target data of the candidate objects; The educational background information, work experience information, and job description information are integrated according to a preset data format to obtain model input data; Using a pre-defined question-and-answer model, the input data of the model is analyzed and processed to generate interview questions; The interview questions are screened using a preset question quality assessment algorithm to obtain target interview questions.

5. The method according to claim 1, characterized in that, The step of using a preset scoring model to score the response data and obtain the scoring results of the candidate objects specifically includes: Extract response features from the response data; The response features are input into a preset scoring model to obtain a preliminary score for the candidate object; Obtain a preset scoring standard, and correct the preliminary score according to the scoring standard to obtain the corrected target score; The target score is output as the score result of the candidate object.

6. The method according to claim 5, characterized in that, The step of selecting candidates for recruitment from the candidate pool based on the scoring results specifically includes: Obtain the reference score of the candidate object; Obtain the reference weight of the reference score, and obtain the target weight of the target score; The reference score, the reference weight, the target score, and the target weight are weighted to obtain the weighted average score of the candidate object. Based on the weighted average score, candidates to be recruited are selected from the candidate candidates.

7. A staffing increase device for an organization, characterized in that, include: The acquisition module is used to acquire recruitment demand information from target organizations and target data of initial candidate objects; The determination module is used to determine candidate objects from the initial candidate objects based on the recruitment demand information, the target data of the initial candidate objects, and the preset reference profile; The question generation module is used to generate target interview questions for the candidate based on the target data of the candidate and using a preset question-and-answer model, so as to obtain the candidate's response data to the target interview questions; The scoring module is used to score the response data using a preset scoring model to obtain the scoring results of the candidate object; The selection module is used to select candidates for recruitment from the candidate candidates based on the scoring results. The employee data acquisition module is used to acquire historical employee data of the target organization; The data mining module is used to extract association rules between the attribute characteristics and career development paths of target employees from historical employee data using a preset association rule mining algorithm. The vector extraction module is used to extract the attribute feature vectors of the target data of the object to be recruited; The path determination module is used to determine the target career development path from career development paths based on association rules and attribute feature vectors; The reference generation module is used to generate career development references for candidates to be recruited, based on their target career development paths.

8. A computer device, characterized in that, It includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of the organizational staffing method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-readable instructions that, when executed by a processor, implement the steps of the organizational staffing method as described in any one of claims 1 to 6.

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