Electronic recruitment matching method and system based on Internet of Things

Abstract

The invention discloses an electronic recruitment matching method and system based on the Internet of Things, and relates to the technical field of electronic recruitment. When a target enterprise performs recruitment, the recruitment quality of recruitment of the target enterprise is analyzed, when the recruitment quality of the recruitment of the target enterprise is low, an upgrading service is provided for the target enterprise, and then during subsequent recruitment, the recruitment quality of the recruitment of the target enterprise is improved. If the target job seeker is matched with the target enterprise, according to the matching condition between the target job seeker and each competitive enterprise, the recommendation frequency of the target enterprise at the target job seeker side is increased, a targeted recommendation scheme at the job seeker side is provided for the target enterprise, the exposure of the target enterprise at the target job seeker side is increased, and the recommendation efficiency of the target enterprise is improved. And the competitiveness and attraction of the target enterprise are improved, so that the probability of preferential recruitment of the enterprise is improved, a basis is provided for recruitment of high-quality job seekers by the enterprise, the recruitment quality of the enterprise is guaranteed, the probability of recruitment of high-quality job seekers by competitive enterprises is reduced, and the comprehensive strength of the enterprise is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic recruitment, and in particular to an electronic recruitment matching method and system based on the Internet of Things. Background Art

[0002] In electronic recruitment, companies often receive a large number of resumes from job seekers and then screen them. Electronic recruitment matching methods can quickly screen out resumes that meet basic requirements, greatly saving the time and energy of manual screening. For job seekers, electronic recruitment matching methods can accurately recommend multiple suitable positions for them based on their resume information and job search intentions, so that they can find job opportunities that meet their expectations more quickly, improving job search efficiency.

[0003] Prior art, such as the invention patent application with publication number CN109558429B, discloses a two-way recommendation method for talent services based on Internet big data, including three parts: data access module, data matching computing cluster and data distribution module, wherein: a job search and recruitment behavior database and a resume position business database are provided in the data access module; an active user cache, a data text analysis result cache, a job seeker matching operator and a position matching operator are provided in the data matching computing cluster; a result database is provided in the data distribution module, and the specific implementation steps are: the first step: data understanding, the second step: data cleaning; the third step: feature selection and evaluation; the fourth step: calculation and storage; the fifth step: recommendation; the sixth step: increment and update data. The present invention captures the user's behavior, collects talent and position information in real time, realizes real-time and accurate matching of massive resumes and position data, and can recommend positions that are more in line with the job seeker's intention to the user, greatly improving service efficiency.

[0004] Prior art, such as an invention patent application with publication number CN115934899A, discloses a resume recommendation method, device, electronic device and storage medium for the IT industry, wherein the method includes: obtaining enterprise recruitment texts and multiple job seeker resume texts and preprocessing them; dividing the preprocessed enterprise recruitment texts and job seeker resume texts into multiple different text blocks, and setting corresponding recruitment preference weighted values ​​for different text blocks according to recruitment requirements; calculating the matching degree between the text block of each enterprise recruitment text and the text block of the corresponding job seeker resume text; multiplying the matching degree of each text block by the corresponding recruitment preference weighted value and adding them to obtain the similarity of multiple enterprise recruitment texts and job seeker resume texts, sorting them in descending order according to the similarity, and selecting the job seeker resume text with the top ranking as the recommended resume. The present invention solves the problem that it is difficult to recommend resumes in an individualized manner in the IT industry, realizes efficient and accurate matching of resumes and recruitment positions, and improves the work efficiency of enterprise recruiters.

[0005] Prior art, such as the invention patent application with publication number CN114943517A, discloses an electronic recruitment matching method, system and storage medium based on the Internet of Things. S1: Obtain preliminary resume information of the target position on the job search and recruitment platform; S2: Extract the first field, second field, third field and fourth field of the preliminary resume information; wherein, the first field is the identity information of the job seeker, the second field is the educational background information of the job seeker, the third field is the work experience information of the job seeker, and the fourth field is the job seeker's work skills profile information; S3: Use the preset model of the pre-set valid resume information to match and screen the preliminary resume information, and determine that the preliminary resume information whose contents of the first field, the second field, the third field and the fourth field are not blank is valid resume information; S4: Analyze the valid resume information, and determine the sorting of the valid resume information according to the number of characters in each field; S5: Push all valid resumes in order according to the sorting results.

[0006] The above scheme has at least the following deficiencies: 1. When job seekers are looking for jobs, they may be matched with multiple companies, and there is competition among multiple companies. Increasing the exposure of companies to job seekers helps to deepen the impression of companies on job seekers, thereby increasing the probability of successful recruitment by companies. However, the above scheme lacks analysis on how to improve the competitiveness of companies and increase the success rate of recruitment when multiple companies compete, thereby failing to increase the competitiveness of companies during recruitment, failing to attract high-quality job seekers for the company, and reducing the effectiveness of recruitment.

[0007] 2. The compatibility between job seekers and different companies is different. Therefore, providing companies with targeted and personalized recommendation services based on the compatibility between job seekers and companies can increase the company's exposure to job seekers and increase the company's competitiveness among job seekers. However, the above plan only analyzes the matching degree between the company and job seekers, and does not set up a recommendation plan for the company on the job seeker side. It cannot improve the company's competitiveness among job seekers, and thus cannot increase the probability of priority recruitment for the company, reduce the company's effectiveness in recruiting high-quality job seekers, and increase the time cost of recruitment. Summary of the invention

[0008] In view of the above-mentioned technical deficiencies, the purpose of the present invention is to provide an electronic recruitment matching method and system based on the Internet of Things.

[0009] In order to solve the above technical problems, the present invention adopts the following technical solutions: In the first aspect, the present invention provides an electronic recruitment matching method based on the Internet of Things, comprising the following steps: S1, recruitment data acquisition: obtaining the recruitment introduction data, intended job seeker data and adapted job seeker data of the recruitment positions of the target enterprise, and obtaining the recruitment introduction data, intended job seeker data and adapted job seeker data of the recruitment positions of each competing enterprise.

[0010] S2. Recruitment data analysis: Utilize the recruitment introduction data, potential job seeker data and suitable job seeker data of the recruitment positions of the target enterprise, as well as the recruitment introduction data and potential job seeker data of the recruitment positions of each competing enterprise, to analyze the recruitment quality of the recruitment positions of the target enterprise, where the recruitment quality includes high quality and low quality. When the recruitment quality of the recruitment positions of the target enterprise is low, execute S3.

[0011] S3, recruitment upgrade analysis: obtain the target company's recruitment job posting data, analyze the target company's recruitment job post recruitment needs, where the recruitment needs include high demand and general demand, and at the same time obtain the recruitment job post upgrade services and release data of each competing company, select the target upgrade service for the target company's recruitment job, and display and upgrade prompts. If the target company chooses to upgrade, when the target job seeker is looking for a job, execute S4.

[0012] S4. Recruitment matching: When the target job seeker is looking for a job, the resume data of the target job seeker is obtained, and the matching situation between the target job seeker and the target enterprise is analyzed. If the matching situation is a match, the target enterprise's recommended data on the target job seeker side is set based on the target enterprise's upgraded service. If the matching situation is a mismatch, no setting is performed.

[0013] In a second aspect, the present invention provides an electronic recruitment matching system based on the Internet of Things, including: a recruitment data acquisition module, used to obtain recruitment introduction data, intended job seeker data and adapted job seeker data of recruitment positions of a target enterprise, and to obtain recruitment introduction data, intended job seeker data and adapted job seeker data of recruitment positions of each competing enterprise.

[0014] The recruitment data analysis module is used to utilize the recruitment introduction data, potential job seeker data and suitable job seeker data of the recruitment positions of the target enterprise, as well as the recruitment introduction data and potential job seeker data of the recruitment positions of each competing enterprise, to analyze the recruitment quality of the recruitment positions of the target enterprise, where the recruitment quality includes high quality and low quality. When the recruitment quality of the recruitment positions of the target enterprise is low, the recruitment upgrade analysis module is executed.

[0015] The recruitment upgrade analysis module is used to obtain the target company's recruitment job posting data, analyze the target company's recruitment job posting recruitment needs, where the recruitment needs include high demand and general demand, and at the same time obtain the upgrade services and release data of each competing company's recruitment job postings, select target upgrade services for the target company's recruitment job postings, and display and upgrade prompts. If the target company chooses to upgrade, when the target job seeker is looking for a job, the recruitment matching module is executed.

[0016] The recruitment matching module is used to obtain the resume data of the target job seeker when the target job seeker is looking for a job, and analyze the matching situation between the target job seeker and the target enterprise. If the matching situation is a match, the recommended data of the target enterprise on the target job seeker side is set based on the upgraded service of the target enterprise. If the matching situation is a mismatch, no setting is performed.

[0017] The beneficial effects of the present invention are as follows: the present invention provides an electronic recruitment matching method and system based on the Internet of Things. When a target enterprise conducts recruitment, the recruitment quality of the target enterprise's recruitment is analyzed. When the recruitment quality of the target enterprise's recruitment is low, an upgrade service is provided to the target enterprise. Then, in subsequent recruitment, if the target job seeker matches the target enterprise, the recommendation frequency of the target enterprise on the target job seeker side is increased according to the matching situation between the target job seeker and each competing enterprise, a targeted recommendation plan on the job seeker side is provided to the target enterprise, the exposure of the target enterprise on the target job seeker side is increased, the competitiveness and attractiveness of the target enterprise are increased, thereby increasing the probability of priority recruitment for the enterprise, providing a basis for the enterprise to recruit high-quality job seekers, ensuring the recruitment quality of the enterprise, and reducing the probability of competing enterprises recruiting high-quality job seekers, which helps to improve the comprehensive strength of the enterprise. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 The present invention is a schematic flow chart of the steps for implementing the method.

[0020] Figure 2 It is a schematic diagram of the system structure connection of the present invention. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on 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.

[0022] Embodiment 1: See also Figure 1As shown, an electronic recruitment matching method based on the Internet of Things includes the following steps: S1, recruitment data acquisition: acquiring recruitment introduction data, intended job seeker data and adapted job seeker data of recruitment positions of the target enterprise, and acquiring recruitment introduction data, intended job seeker data and adapted job seeker data of recruitment positions of each competing enterprise.

[0023] It should be noted that recruitment introduction data includes job introduction, job requirements and welfare benefits of recruitment positions; potential job seekers are job seekers who are considered suitable for the recruitment positions, that is, job seekers who actively communicate with recruiters; suitable job seekers are job seekers who are suitable for the recruitment platform and the company's recruitment positions, that is, job seekers who actively communicate with recruiters.

[0024] The recruitment platform will use the job seekers' resumes and the job requirements of the company's recruitment positions to analyze the compatibility between the job seekers and the company's recruitment positions. When a match is found, the job seekers will be given priority to the recruiting personnel, who will take the initiative to communicate. For details on the analysis of the compatibility between job seekers and company recruitment positions, please refer to the invention patent with publication number CN118798836A, entitled "Precision Matching Recruitment System Based on Big Data Analysis", which will not be repeated here.

[0025] The data of potential job seekers includes the resume data and click frequency of each potential job seeker, among which the resume data refers to the content of the resume. The click frequency refers to the number of times the main interface of the recruitment position introduction is clicked.

[0026] The suitable job seeker data includes the resume content of each suitable job seeker.

[0027] S2. Recruitment data analysis: Utilize the recruitment introduction data, potential job seeker data and suitable job seeker data of the recruitment positions of the target enterprise, as well as the recruitment introduction data and potential job seeker data of the recruitment positions of each competing enterprise, to analyze the recruitment quality of the recruitment positions of the target enterprise, where the recruitment quality includes high quality and low quality. When the recruitment quality of the recruitment positions of the target enterprise is low, execute S3.

[0028] In a specific embodiment, the specific process of analyzing the recruitment quality of the target enterprise's recruitment positions is as follows: obtaining the resume data of each intended job seeker from the intended job seeker data of the target enterprise's recruitment positions, and using the recruitment introduction data of the target enterprise's recruitment positions to calculate the score of each intended job seeker in the target enterprise's recruitment positions, obtaining each suitable job seeker from the suitable job seeker data of the target enterprise's recruitment positions, comparing each intended job seeker with each suitable job seeker, and treating each intended job seeker that is the same as each suitable job seeker and each suitable job seeker as high-quality job seekers in the target enterprise's recruitment positions.

[0029] It should be noted that the score of each potential job seeker in the target company's recruitment position represents the matching coefficient between the target company's recruitment position and each potential job seeker. The specific calculation method is the same as the calculation method of the matching coefficient between the resume to be matched and the target position in the invention patent with publication number CN118798836A and name "Precision Matching Recruitment System Based on Big Data Analysis", which will not be repeated here.

[0030] At the same time, the resume data of each potential job seeker is obtained from the data of potential job seekers for recruitment positions of each competing enterprise. According to the calculation method of the score of each potential job seeker in the recruitment position of the target enterprise, the score of each potential job seeker in the recruitment position of each competing enterprise is calculated, and at the same time, each high-quality job seeker in the recruitment position of each competing enterprise is obtained.

[0031] The scores of each intended job seeker in the recruitment positions of the target enterprise, the number of high-quality job seekers, and the scores of each intended job seeker in the recruitment positions of each competing enterprise and the number of high-quality job seekers are input into the quality assessment model, and the recruitment quality assessment results of the recruitment positions of the target enterprise are output.

[0032] The recruitment quality assessment results include values ​​of 1 and 0. When the recruitment quality assessment result is 1, it indicates that the recruitment quality of the target enterprise's recruitment positions is high. Conversely, when the recruitment quality assessment result is 0, it indicates that the recruitment quality of the target enterprise's recruitment positions is low.

[0033] In the above, the expression of the quality assessment model is: ,in, represents the recruitment quality assessment result of the target enterprise's recruitment positions, FZ represents the average score of each intended job seeker in the target enterprise's recruitment positions, Indicates the proportion of high-quality job seekers in the recruitment positions of the target company, The ratio of the number of high-quality job seekers to the number of suitable job seekers in the recruitment positions of the target enterprise. represents the average score of each candidate in the recruitment position of the jth competing enterprise, represents the proportion of high-quality job seekers in the recruitment positions of the j-th competing enterprise, is the ratio of the number of high-quality job seekers to the number of suitable job seekers in the recruitment positions of the j-th competing enterprise, j represents the number of each competing enterprise, j is a positive integer, and J represents the number of competing enterprises. It is the preset recruitment quality assessment threshold.

[0034] It should be noted that the preset recruitment quality assessment threshold is the critical value for judging whether the recruitment quality is high. It is set by the staff in the recruitment platform. When it is greater than or equal to the recruitment quality assessment threshold, it indicates that the recruitment quality is high, otherwise the recruitment quality is low.

[0035] in, The calculation result is the recruitment quality assessment value of the target enterprise's recruitment position.

[0036] S3, recruitment upgrade analysis: obtain the target company's recruitment job posting data, analyze the target company's recruitment job post recruitment needs, where the recruitment needs include high demand and general demand, and at the same time obtain the recruitment job post upgrade services and release data of each competing company, select the target upgrade service for the target company's recruitment job, and display and upgrade prompts. If the target company chooses to upgrade, when the target job seeker is looking for a job, execute S4.

[0037] Among them, the target company's job posting data is obtained from the background of the recruitment platform. The target company's job posting data includes the release time, closing time, number of recruits and daily online recruitment time of each recruitment.

[0038] In a specific embodiment, the specific process of analyzing the recruitment needs of the recruitment positions of the target enterprise is as follows: S31, obtaining the release time, closing time, number of recruits and daily online recruitment time of each recruitment from the release data of the recruitment positions of the target enterprise; according to the release time and closing time of each recruitment of the recruitment positions of the target enterprise, obtaining the duration of each recruitment of the recruitment positions of the target enterprise; according to the release time of each recruitment of the recruitment positions of the target enterprise, obtaining the recruitment frequency of the recruitment positions of the target enterprise.

[0039] S32. Obtain the recruitment frequency, duration of each recruitment, number of recruits and single-day online recruitment duration of each competing enterprise's recruitment positions from the recruitment platform, and then calculate the average respectively as the reference recruitment frequency, reference recruitment duration, reference number of recruits and reference single-day online recruitment duration, respectively recorded as , , and .

[0040] S33. Recruitment demand assessment model expression: , where represents the recruitment demand assessment result of the target enterprise's recruitment positions, e represents a natural constant, Indicates the recruitment frequency of the target company's recruitment positions, , , They represent the duration of the recruitment of the target enterprise in the cth recruitment, the number of recruits, and the online recruitment time per day, c represents the number of each recruitment, c represents a positive integer, and C represents the total number of recruitments. Evaluate thresholds for pre-set hiring needs.

[0041] =1, indicating that the recruitment demand of the target enterprise's recruitment position is high, =-1, indicating that the recruitment demand of the target company's recruitment positions is general demand.

[0042] It should be noted that the preset recruitment demand assessment threshold is the critical value for judging whether the recruitment demand is high. It is set by the staff in the recruitment platform. When it is greater than or equal to the preset recruitment demand assessment threshold, it indicates that the recruitment demand is high, otherwise it is general demand.

[0043] The recruitment platform provides a variety of upgrade services for companies with different needs. The validity period, permissions and promotion volume of each upgrade service vary. When the recruitment quality is low, users can choose the upgrade service to upgrade their account and improve the recruitment quality.

[0044] In another specific embodiment, the target upgrade service is selected for the recruitment positions of the target enterprise, and the specific process is as follows: each initial upgrade service corresponding to the high demand is obtained from the recruitment platform, and as each initial upgrade service, the recruitment quality assessment value, recruitment attraction value, recruitment effect value of each upgraded enterprise in each initial upgrade service and the recruitment attraction value of each competing enterprise are obtained.

[0045] It should be noted that the recruitment effect value is the recruitment quality assessment value after the upgrade service minus the recruitment quality assessment value before the upgrade service, and then divided by the recruitment quality assessment value before the upgrade service.

[0046] Using the recruitment introduction data of the target enterprise's recruitment positions, the recruitment introduction data and upgrade services of each competing enterprise, the recruitment attraction value of the target enterprise and the recruitment attraction value of each competing enterprise are calculated respectively, and the recruitment quality assessment value of the target enterprise's recruitment positions is extracted at the same time; then the adaptation value of each initial upgrade service and the target enterprise is calculated, and the initial upgrade service with the largest adaptation value is selected as the target upgrade service.

[0047] In the above, the recruitment attraction value of the target enterprise is calculated. The specific calculation process is: obtain the click frequency of each intended job seeker in the target enterprise's recruitment position from the intended job seeker data of the target enterprise's recruitment position, recorded as , the click frequency of each intended job seeker in each competing company's recruitment position is obtained from the intended job seeker data of each competing company's recruitment position, recorded as At the same time, the scores of each intended job seeker in the recruitment positions of the target enterprise and the scores of each intended job seeker in the recruitment positions of each competing enterprise are obtained, which are recorded as and , j represents the number of each competing enterprise, j is a positive integer, p represents the score of each intended job seeker, p is a positive integer.

[0048] Using the calculation formula: , get the recruitment attraction value of the target enterprise , where J represents the number of competing companies, P represents the number of intended job seekers, and e represents a natural constant.

[0049] It should be noted that the calculation of the recruitment attraction value of each competing enterprise is the same as that of the target enterprise.

[0050] Preferably, the adaptation value calculation process of each initial upgrade service and the target enterprise is as follows: the recruitment attraction value of each upgraded enterprise in each initial upgrade service is divided by the average recruitment attraction value of each competing enterprise to obtain the recruitment attraction value of each upgraded enterprise in each initial upgrade service, which is recorded as , where q represents the number of each initial upgrade service, y represents the number of each upgrade enterprise, and q and y are positive integers; At the same time, the recruitment quality evaluation value and recruitment effect value of each upgraded enterprise in each initial upgrade service are recorded as and , and record the recruitment quality assessment value of the target enterprise's recruitment position as , and then use the calculation formula: , and obtain the adaptation value of the qth initial upgrade service and the target enterprise , where Y and Q represent the number of upgraded enterprises and the number of initial upgraded services, respectively.

[0051] S4. Recruitment matching: When the target job seeker is looking for a job, the resume data of the target job seeker is obtained, and the matching situation between the target job seeker and the target enterprise is analyzed. If the matching situation is a match, the target enterprise's recommended data on the target job seeker side is set based on the target enterprise's upgraded service. If the matching situation is a mismatch, no setting is performed.

[0052] In the above, analyzing the matching between the target job seeker and the target enterprise means analyzing the matching between the target job seeker's resume and the target enterprise's recruitment position. The specific analysis method is consistent with the method of judging whether the person-job matching is consistent in the invention patent with publication number CN118798836A and name "Precise Matching Recruitment System Based on Big Data Analysis", which will not be repeated here.

[0053] In a specific embodiment, the target enterprise's recommendation data for the target job seeker is set, and the specific process is as follows: using the target enterprise's recruitment introduction data and the target job seeker's resume data, the target job seeker's score in the target enterprise is obtained, and similarly, the target job seeker's score in each competing enterprise is obtained, and then the target enterprise and each competing enterprise are analyzed respectively. The initial competition level on the target job seeker's side, where the initial competition level is level one, level two, and level three.

[0054] Preferably, the initial competition level analysis process of the target enterprise on the target job seeker side is as follows: take the score of the target job seeker in the target enterprise as the target score, obtain the number of intended job seekers and the number of suitable job seekers with the target score when the target enterprise is recruiting from the background of the recruitment platform, add up to get the number of marked job seekers, and at the same time obtain the number of already employed people with the target score when the target enterprise is recruiting from the background of the recruitment platform, divide the number of already employed people by the number of marked job seekers to get the employment ratio of the target enterprise, and similarly obtain the employment ratio of each competing enterprise.

[0055] The maximum and minimum entry ratios are selected from the target company's and each competing company's entry ratios respectively, then the maximum and minimum entry ratios are added and divided by 2 to obtain the middle entry ratio. At the same time, the mode is selected from the target company's and each competing company's entry ratios as the mode entry ratio. Then the maximum, minimum, middle and mode entry ratios are sorted in descending order, and recorded as the first, second, third and fourth entry ratios respectively.

[0056] Among them, the interval between the first entry ratio and the second entry ratio corresponds to the first-level entry ratio interval in the initial competition level, the interval between the second entry ratio and the third entry ratio corresponds to the second-level entry ratio interval in the initial competition level, and the interval between the third entry ratio and the fourth entry ratio corresponds to the third-level entry ratio interval in the initial competition level. Then, based on the entry ratio of the target enterprise, the initial competition level of the target enterprise in relation to the target job seeker is obtained.

[0057] If the target enterprise's initial competition level on the target job seeker side is level one, obtain each competing enterprise with an initial competition level of level one on the target job seeker side as each marked enterprise, obtain the average recommendation frequency of each marked enterprise on the job seeker side from the background of the recruitment platform, and then select the maximum average recommendation frequency as the reference recommendation frequency, extract the target enterprise's recruitment attraction value and the recruitment attraction value of each marked enterprise, if the target enterprise's recruitment attraction value is greater than the recruitment attraction value of each marked enterprise, set the target enterprise's recommendation frequency on the target job seeker side to 1.2 times the reference recommendation frequency, if the target enterprise's recruitment attraction value is less than the recruitment attraction value of at least one marked enterprise, set the target enterprise's recommendation frequency on the target job seeker side to 1.5 times the reference recommendation frequency.

[0058] If the initial competition level of the target enterprise on the target job seeker side is level 2, set the target enterprise's recommendation frequency on the target job seeker side to 1.8 times the reference recommendation frequency.

[0059] If the initial competition level of the target enterprise on the target job seeker side is level three, set the target enterprise's recommendation frequency on the target job seeker side to twice the reference recommendation frequency, so as to obtain the target enterprise's recommendation frequency on the target job seeker side as the recommendation data.

[0060] Embodiment 2: See also Figure 2 As shown, an electronic recruitment matching system based on the Internet of Things includes: a recruitment data acquisition module, a recruitment data analysis module, a recruitment upgrade analysis module and a recruitment matching module.

[0061] The recruitment data acquisition module is used to obtain the recruitment introduction data, intended job seeker data and suitable job seeker data of the recruitment positions of the target enterprise, and to obtain the recruitment introduction data, intended job seeker data and suitable job seeker data of the recruitment positions of each competing enterprise.

[0062] The recruitment data analysis module is used to utilize the recruitment introduction data, potential job seeker data and suitable job seeker data of the recruitment positions of the target enterprise, as well as the recruitment introduction data and potential job seeker data of the recruitment positions of each competing enterprise, to analyze the recruitment quality of the recruitment positions of the target enterprise, where the recruitment quality includes high quality and low quality. When the recruitment quality of the recruitment positions of the target enterprise is low, the recruitment upgrade analysis module is executed.

[0063] The recruitment upgrade analysis module is used to obtain the target company's recruitment job posting data, analyze the target company's recruitment job posting recruitment needs, where the recruitment needs include high demand and general demand, and at the same time obtain the upgrade services and release data of each competing company's recruitment job postings, select target upgrade services for the target company's recruitment job postings, and display and upgrade prompts. If the target company chooses to upgrade, when the target job seeker is looking for a job, the recruitment matching module is executed.

[0064] The recruitment matching module is used to obtain the resume data of the target job seeker when the target job seeker is looking for a job, and analyze the matching situation between the target job seeker and the target enterprise. If the matching situation is a match, the recommended data of the target enterprise on the target job seeker side is set based on the upgraded service of the target enterprise. If the matching situation is a mismatch, no setting is performed.

[0065] According to the embodiment of the present invention, when the target enterprise is recruiting, the recruitment quality of the target enterprise's recruitment is analyzed. When the recruitment quality of the target enterprise's recruitment is low, an upgrade service is provided to the target enterprise. Then, in subsequent recruitment, if the target job seeker matches the target enterprise, the recommendation frequency of the target enterprise to the target job seeker is increased according to the matching situation between the target job seeker and each competing enterprise, and a targeted recommendation plan is provided to the target enterprise on the job seeker side, thereby increasing the exposure of the target enterprise to the target job seeker, increasing the competitiveness and attractiveness of the target enterprise, thereby increasing the probability of priority recruitment for the enterprise, providing a basis for the enterprise to recruit high-quality job seekers, ensuring the recruitment quality of the enterprise, and reducing the probability of competing enterprises recruiting high-quality job seekers, which helps to improve the comprehensive strength of the enterprise.

[0066] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they shall all fall within the protection scope of the present invention.

Claims

1. An electronic recruitment matching method based on the Internet of Things, characterized in that: The steps include: S1. Recruitment data acquisition: Obtain the recruitment introduction data, intended job seeker data and suitable job seeker data of the recruitment positions of the target enterprise, and obtain the recruitment introduction data, intended job seeker data and suitable job seeker data of the recruitment positions of each competing enterprise; S2, recruitment data analysis: using the recruitment introduction data, intended job seeker data and suitable job seeker data of the recruitment positions of the target enterprise, as well as the recruitment introduction data and intended job seeker data of the recruitment positions of each competing enterprise, analyze the recruitment quality of the recruitment positions of the target enterprise, where the recruitment quality includes high quality and low quality. When the recruitment quality of the recruitment positions of the target enterprise is low, execute S3; S3, recruitment upgrade analysis: obtain the target company's recruitment post data, analyze the target company's recruitment post recruitment needs, where the recruitment needs include high demand and general demand, and obtain the upgrade services and release data of each competing company's recruitment post, select the target upgrade service for the target company's recruitment post, and display and upgrade prompts. If the target company chooses to upgrade, when the target job seeker is looking for a job, execute S4; S4. Recruitment matching: When the target job seeker is looking for a job, the resume data of the target job seeker is obtained, and the matching situation between the target job seeker and the target enterprise is analyzed. If the matching situation is a match, the target enterprise's recommended data on the target job seeker side is set based on the target enterprise's upgraded service. If the matching situation is a mismatch, no setting is performed.

2. The method for electronic recruitment matching based on the Internet of Things according to claim 1, characterized in that: The specific process of analyzing the recruitment quality of the target enterprise's recruitment positions is as follows: Obtain the resume data of each intended job seeker from the intended job seeker data of the recruitment position of the target enterprise, and use the recruitment introduction data of the recruitment position of the target enterprise to calculate the score of each intended job seeker to obtain the score of each intended job seeker in the recruitment position of the target enterprise, obtain each suitable job seeker from the suitable job seeker data of the recruitment position of the target enterprise, compare each intended job seeker with each suitable job seeker, and use each intended job seeker and each suitable job seeker that are the same as each suitable job seeker as each high-quality job seeker in the recruitment position of the target enterprise; At the same time, the resume data of each intended job seeker is obtained from the intended job seeker data of the recruitment positions of each competing enterprise, and the score of each intended job seeker in the recruitment position of each competing enterprise is calculated according to the calculation method of the score of each intended job seeker in the recruitment position of the target enterprise, and at the same time, each high-quality job seeker in the recruitment position of each competing enterprise is obtained; Input the scores of each intended job seeker in the recruitment positions of the target enterprise, the number of high-quality job seekers, and the scores of each intended job seeker in the recruitment positions of each competing enterprise and the number of high-quality job seekers into the quality assessment model, and output the recruitment quality assessment results of the recruitment positions of the target enterprise; The recruitment quality assessment results include values ​​of 1 and 0. When the recruitment quality assessment result is 1, it indicates that the recruitment quality of the target enterprise's recruitment positions is high. Conversely, when the recruitment quality assessment result is 0, it indicates that the recruitment quality of the target enterprise's recruitment positions is low.

3. The method for electronic recruitment matching based on the Internet of Things according to claim 2, characterized in that: The expression of the quality assessment model is: ,in, represents the recruitment quality assessment result of the target enterprise's recruitment positions, FZ represents the average score of each intended job seeker in the target enterprise's recruitment positions, Indicates the proportion of high-quality job seekers in the recruitment positions of the target company, The ratio of the number of high-quality job seekers to the number of suitable job seekers in the recruitment positions of the target enterprise. represents the average score of each candidate in the recruitment position of the jth competing enterprise, represents the proportion of high-quality job seekers in the recruitment positions of the j-th competing enterprise, is the ratio of the number of high-quality job seekers to the number of suitable job seekers in the recruitment positions of the j-th competing enterprise, j represents the number of each competing enterprise, j is a positive integer, and J represents the number of competing enterprises. It is the preset recruitment quality assessment threshold.

4. The electronic recruitment matching method based on the Internet of Things according to claim 1 is characterized in that: The specific process of analyzing the recruitment needs of the target company's recruitment positions is as follows: S31. Obtain the time of each recruitment release, closing time, number of recruits, and daily online recruitment duration from the recruitment post data of the target enterprise; obtain the duration of each recruitment for the target enterprise's recruitment posts according to the time of each recruitment release and closing time, and obtain the recruitment frequency of the target enterprise's recruitment posts according to the time of each recruitment release; S32. Obtain the recruitment frequency, duration of each recruitment, number of recruits and single-day online recruitment duration of each competing enterprise's recruitment positions from the recruitment platform, and then calculate the average respectively as the reference recruitment frequency, reference recruitment duration, reference number of recruits and reference single-day online recruitment duration, respectively recorded as , , and ; S33. Recruitment demand assessment model expression: , where represents the recruitment demand assessment result of the target enterprise's recruitment positions, e represents a natural constant, Indicates the recruitment frequency of the target company's recruitment positions, , , They represent the duration of the recruitment of the target enterprise in the cth recruitment, the number of recruits, and the online recruitment time per day, c represents the number of each recruitment, c represents a positive integer, and C represents the total number of recruitments. Evaluate thresholds for pre-set hiring needs; =1, indicating that the recruitment demand of the target enterprise's recruitment position is high, =-1, indicating that the recruitment demand of the target company's recruitment positions is general demand.

5. The electronic recruitment matching method based on the Internet of Things according to claim 1 is characterized in that: The above is a target upgrade service for the recruitment positions of the target enterprise. The specific process is as follows: Obtain each initial upgrade service corresponding to high demand from the recruitment platform as each initial upgrade service, and obtain the recruitment quality assessment value, recruitment attraction value, recruitment effect value of each upgraded enterprise in each initial upgrade service and the recruitment attraction value of each competing enterprise; Using the recruitment introduction data of the target company's recruitment positions, the recruitment introduction data of each competing company and the upgrade services, the recruitment attraction value of the target company and the recruitment attraction value of each competing company are calculated respectively, and the recruitment quality assessment value of the target company's recruitment positions is extracted at the same time; Then calculate the adaptation value of each initial upgrade service and the target enterprise, and select the initial upgrade service with the largest adaptation value as the target upgrade service.

6. The method for electronic recruitment matching based on the Internet of Things according to claim 5, characterized in that: Calculate the recruitment attraction value of the target enterprise. The specific calculation process is as follows: From the target company's recruitment position's intended job seeker data, obtain the click frequency of each intended job seeker in the target company's recruitment position, recorded as , the click frequency of each intended job seeker in each competing company's recruitment position is obtained from the intended job seeker data of each competing company's recruitment position, recorded as At the same time, the scores of each intended job seeker in the recruitment positions of the target enterprise and the scores of each intended job seeker in the recruitment positions of each competing enterprise are obtained, which are recorded as and , j represents the number of each competing enterprise, j is a positive integer, p represents the score of each intended job seeker, p is a positive integer, Using the calculation formula: , get the recruitment attraction value of the target enterprise , where J represents the number of competing companies, P represents the number of intended job seekers, and e represents a natural constant.

7. The method for electronic recruitment matching based on the Internet of Things according to claim 6, characterized in that: The calculation process of the adaptation value between each initial upgrade service and the target enterprise is as follows: The recruitment attraction value of each upgraded enterprise in each initial upgrade service is divided by the average recruitment attraction value of each competing enterprise to obtain the recruitment attraction value of each upgraded enterprise in each initial upgrade service, which is recorded as , where q represents the number of each initial upgrade service, y represents the number of each upgrade enterprise, and q and y are positive integers; At the same time, the recruitment quality evaluation value and recruitment effect value of each upgraded enterprise in each initial upgrade service are recorded as and , and record the recruitment quality assessment value of the target enterprise's recruitment position as , and then use the calculation formula: , and obtain the adaptation value of the qth initial upgrade service and the target enterprise , where Y and Q represent the number of upgraded enterprises and the number of initial upgraded services, respectively.

8. The method for electronic recruitment matching based on the Internet of Things according to claim 1, characterized in that: The specific process of setting the target enterprise's recommendation data on the target job seeker is as follows: Using the recruitment introduction data of the recruitment positions of the target enterprise and the resume data of the target job seeker, the score of the target job seeker in the target enterprise is obtained. Similarly, the score of the target job seeker in each competing enterprise is obtained. Then, the initial competition level of the target enterprise and each competing enterprise on the target job seeker side is analyzed, where the initial competition level is level one, level two and level three; If the target enterprise's initial competition level on the target job seeker side is level one, obtain each competing enterprise with an initial competition level of level one on the target job seeker side as each marked enterprise, obtain the average recommendation frequency of each marked enterprise on the job seeker side from the background of the recruitment platform, and then select the maximum average recommendation frequency as the reference recommendation frequency, extract the recruitment attraction value of the target enterprise and the recruitment attraction value of each marked enterprise, if the recruitment attraction value of the target enterprise is greater than the recruitment attraction value of each marked enterprise, set the recommendation frequency of the target enterprise on the target job seeker side to 1.2 times the reference recommendation frequency, if the recruitment attraction value of the target enterprise is less than the recruitment attraction value of at least one marked enterprise, set the recommendation frequency of the target enterprise on the target job seeker side to 1.5 times the reference recommendation frequency; If the target enterprise's initial competition level on the target job seeker side is level 2, set the target enterprise's recommendation frequency on the target job seeker side to 1.8 times the reference recommendation frequency; If the initial competition level of the target enterprise on the target job seeker side is level three, set the target enterprise's recommendation frequency on the target job seeker side to twice the reference recommendation frequency, so as to obtain the target enterprise's recommendation frequency on the target job seeker side as the recommendation data.

9. The method for electronic recruitment matching based on the Internet of Things according to claim 8, characterized in that: The initial competitive level analysis process of the target enterprise on the target job seeker side is as follows: The target job seeker's score at the target company is taken as the target score. The number of intended job seekers and the number of suitable job seekers with the target score at the time of recruitment are obtained from the background of the recruitment platform. The number of marked job seekers is accumulated. At the same time, the number of employed candidates with the target score at the time of recruitment is obtained from the background of the recruitment platform. The number of employed candidates is divided by the number of marked job seekers to obtain the employment ratio of the target company. Similarly, the employment ratio of each competing company is obtained. The maximum and minimum entry ratios are selected from the target company's entry ratios and the entry ratios of each competing company, respectively. Then the maximum and minimum entry ratios are added and divided by 2 to obtain the middle entry ratio. At the same time, the mode is selected from the target company's entry ratios and the entry ratios of each competing company as the mode entry ratio. Then the maximum entry ratio, the minimum entry ratio, the middle entry ratio and the mode entry ratio are sorted in descending order and recorded as the first entry ratio, the second entry ratio, the third entry ratio and the fourth entry ratio respectively. Among them, the interval between the first entry ratio and the second entry ratio corresponds to the first-level entry ratio interval in the initial competition level, the interval between the second entry ratio and the third entry ratio corresponds to the second-level entry ratio interval in the initial competition level, and the interval between the third entry ratio and the fourth entry ratio corresponds to the third-level entry ratio interval in the initial competition level. Then, based on the entry ratio of the target enterprise, the initial competition level of the target enterprise in relation to the target job seeker is obtained.

10. An Internet of Things-based electronic recruitment matching system for executing the Internet of Things-based electronic recruitment matching method according to any one of claims 1 to 9, characterized in that: include: Recruitment data acquisition module, used to obtain recruitment introduction data, intended job seeker data and suitable job seeker data of recruitment positions of target enterprises, and to obtain recruitment introduction data, intended job seeker data and suitable job seeker data of recruitment positions of competing enterprises; The recruitment data analysis module is used to analyze the recruitment quality of the recruitment positions of the target enterprise by using the recruitment introduction data, intended job seeker data and suitable job seeker data of the recruitment positions of the target enterprise, as well as the recruitment introduction data and intended job seeker data of the recruitment positions of each competing enterprise, wherein the recruitment quality includes high quality and low quality. When the recruitment quality of the recruitment positions of the target enterprise is low, the recruitment upgrade analysis module is executed; The recruitment upgrade analysis module is used to obtain the target company's recruitment job posting data, analyze the recruitment needs of the target company's recruitment jobs, where the recruitment needs include high needs and general needs, and obtain the upgrade services and release data of each competing company's recruitment jobs, select target upgrade services for the target company's recruitment jobs, and display and upgrade prompts. If the target company chooses to upgrade, when the target job seeker is looking for a job, the recruitment matching module is executed; The recruitment matching module is used to obtain the resume data of the target job seeker when the target job seeker is looking for a job, and analyze the matching situation between the target job seeker and the target enterprise. If the matching situation is a match, the recommended data of the target enterprise on the target job seeker side is set based on the upgraded service of the target enterprise. If the matching situation is a mismatch, no setting is performed.

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