Human resource post matching method and device

Abstract

The invention relates to the technical field of human resource management, in particular to a human resource post matching method and device. The method comprises the steps of obtaining job seeker data and enterprise post requirements, encrypting and storing the job seeker data and the enterprise post requirements to an alliance chain, verifying a key data hash value through a public block chain, and outputting a verification result; the job seeker data and the enterprise post requirements are inquired and analyzed respectively, and job seeker labels and post labels are output; according to the job seeker label and the post label, calculating a matching index between the job seeker and the enterprise post, and generating a recommendation report; the device comprises a data verification module, a job seeker and enterprise post label generation module and a matching index calculation module. By verifying the hash value of the key data, the feasibility of the data is improved, and the risk caused by data counterfeiting or tampering is reduced, so that the recruitment quality is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of human resource management, and in particular to a human resource position matching method and device. Background Art

[0002] In today's digital age, the recruitment industry is undergoing profound changes. With the rapid development of internet technology, information exchange between companies and job seekers has become more frequent and efficient. However, data security and authenticity remain key issues that need to be addressed during the recruitment process.

[0003] Under the traditional recruitment model, information asymmetry often exists between employers and job seekers. It's difficult for employers to fully understand applicants' true abilities and backgrounds, while applicants struggle to accurately understand the company's job requirements and work environment. Due to the lack of effective data verification mechanisms, resume fraud and data tampering are commonplace, harming the interests of both parties and reducing recruitment efficiency and quality.

[0004] In summary, it is very necessary to propose a human resource job matching method and device that can improve the feasibility of data, reduce the risks caused by data falsification or tampering, and thus improve the quality of recruitment. Summary of the Invention

[0005] The purpose of the present invention is to provide a human resources job matching method and device, aiming to improve the feasibility of data, reduce the risks caused by data falsification or tampering, and thus improve the quality of recruitment.

[0006] To achieve the above object, the present invention adopts a human resource position matching method, comprising the following steps:

[0007] Obtain job seeker data and company job requirements, encrypt and store them in the alliance chain, verify the key data hash value through the public blockchain, and output the verification results;

[0008] Query job seeker data and company job requirements respectively, analyze them, and output job seeker tags and job tags;

[0009] Based on the job seeker tags and job tags, the matching index between the employee and the company's job is calculated, and a recommendation report is generated.

[0010] Among them, in the steps of obtaining job seeker data and enterprise job requirements, encrypting and storing them in the alliance chain, verifying the key data hash value through the public blockchain, and outputting the verification results:

[0011] Obtain job seeker information and enterprise job demand data respectively, encrypt the data, and store it in the alliance chain;

[0012] Calculate the hash value of key data in job seeker information and enterprise job demand data, determine whether key data has been tampered with, and output the judgment result.

[0013] Among them, the steps of obtaining job seeker information and enterprise job demand data, encrypting the data, and storing it in the alliance chain are as follows:

[0014] Job applicant information includes educational background, work experience, skills and expertise, project experience, professional qualification certificates, and interview evaluation records; company job requirements include job title, job responsibilities, job requirements, salary and benefits, and work location.

[0015] Among them, in the step of calculating the hash value of key data in the job seeker information and the enterprise job demand data, judging whether the key data has been tampered with, and outputting the judgment result:

[0016] When the judgment result is that the key data has not been tampered with, a request for analyzing the job seeker data and corporate job requirements is triggered;

[0017] When the judgment result is that key data has been tampered with, a tampering tag is generated for the key data, the tampering tag is identified and an early warning is issued.

[0018] Among them, in the steps of querying job seeker data and enterprise job requirements, analyzing them, and outputting job seeker tags and job tags:

[0019] Query the encrypted data of job seekers stored in the alliance chain, decrypt it, and obtain the original data of the job seekers;

[0020] Analyze various data of job seekers and generate job seeker tags based on the analysis results.

[0021] Among them, in the steps of querying job seeker data and enterprise job requirements, analyzing them, and outputting job seeker tags and job tags:

[0022] Query the encrypted data of enterprise job requirements stored in the alliance chain, decrypt it, and obtain the original data of enterprise job requirements;

[0023] Analyze the company's job requirements and generate job tags based on the analysis results.

[0024] Among them, in the step of calculating the matching index between the employee and the company's position based on the job seeker tag and the position tag, and generating a recommendation report:

[0025] Get job seeker tags and job tags;

[0026] Calculate the matching index between job seekers and company positions based on job seeker tags and position tags, and output the matching degree.

[0027] Among them, before the step of calculating the matching index between the job seeker and the company position based on the job seeker tag and the position tag and outputting the matching degree:

[0028] A matching index range is established, and multiple matching degrees are divided according to the matching index range.

[0029] After calculating the matching index between the job seeker and the company's position based on the job seeker tag and the position tag and outputting the matching degree:

[0030] Based on the matching degree between multiple job seekers and positions, a job seeker recommendation report for the position is output.

[0031] The present invention also provides a human resources position matching device, comprising a data verification module, a job seeker and enterprise position label generation module, and a matching index calculation module; wherein:

[0032] The data verification module is used to obtain job seeker data and enterprise job requirements, encrypt and store them in the alliance chain, verify the key data hash value through the public blockchain, and output the verification result;

[0033] The job seeker and enterprise position label generation module is used to query job seeker data and enterprise position requirements respectively, analyze them, and output job seeker labels and position labels;

[0034] The matching index calculation module is used to calculate the matching index between the employee and the enterprise position according to the job seeker tag and the position tag, and generate a recommendation report.

[0035] A human resources job matching method and device of the present invention respectively adopts the data verification module, the job seeker and enterprise job label generation module, and the matching index calculation module to perform the following steps: obtaining job seeker data and enterprise job requirements, and encrypting and storing them in a consortium chain, verifying key data hash values ​​through a public blockchain, and outputting verification results; querying job seeker data and enterprise job requirements respectively, analyzing them, and outputting job seeker labels and job labels; calculating the matching index between the employee and the enterprise position based on the job seeker label and job label, and generating a recommendation report; by verifying key data hash values, improving the feasibility of data, reducing the risks caused by data falsification or tampering, and thus improving recruitment quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. 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 any creative work.

[0037] Figure 1 It is a flow chart of the human resources position matching method of the present invention.

[0038] Figure 2 It is a flowchart of the steps of the human resources position matching method of the present invention.

[0039] Figure 3 It is a step flow chart of S100 of the present invention.

[0040] Figure 4 It is a step flow chart of S200 of the present invention.

[0041] Figure 5 It is a step flow chart of S300 of the present invention.

[0042] Figure 6 It is a structural principle diagram of the human resources position matching device of the present invention.

[0043] Figure 7 It is a structural principle diagram of the electronic device of the present invention.

[0044] 401-Data verification module, 402-Job seeker and enterprise position label generation module, 403-Matching index calculation module. DETAILED DESCRIPTION

[0045] Exemplary embodiments are described in detail herein, with examples illustrated in the accompanying drawings. When the following description refers to the drawings, identical numerals in different drawings represent identical or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with this application.

[0046] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. As used in this application and the appended claims, the singular forms "a," "an," "the," and "the" are intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0047] It should be understood that although the terms first, second, third, etc. may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0048] See also Figures 1 to 5 The present invention provides a human resource position matching method, comprising the following steps:

[0049] S100: Obtain job seeker data and company job requirements, encrypt and store them in the alliance chain, verify the key data hash value through the public blockchain, and output the verification result;

[0050] S200: Query job seeker data and enterprise job requirements respectively, perform analysis, and output job seeker tags and job tags;

[0051] S300: Calculate the matching index between the employee and the company's position based on the job seeker tag and the position tag, and generate a recommendation report.

[0052] In this implementation, job seeker data and company job requirements are first obtained and encrypted and stored in the alliance chain. The key data hash value is verified through the public blockchain, and the verification result is output. Then, the job seeker data and company job requirements are queried separately, analyzed, and the job seeker label and job label are output. Finally, based on the job seeker label and job label, the matching index between the employee and the company position is calculated, and a recommendation report is generated. By verifying the key data hash value, the feasibility of the data is improved, and the risks caused by data falsification or tampering are reduced, thereby improving the quality of recruitment.

[0053] Furthermore, in the steps of obtaining job seeker data and enterprise job requirements, encrypting and storing them in the consortium chain, verifying the key data hash value through the public blockchain, and outputting the verification result:

[0054] S101: Obtain job seeker information and enterprise job demand data respectively, encrypt the data, and store them in the alliance chain;

[0055] S102: Calculate the hash value of key data in the job seeker information and the enterprise job requirement data, determine whether the key data has been tampered with, and output the determination result.

[0056] Furthermore, in the steps of obtaining job seeker information and enterprise job demand data, encrypting the data, and storing them in the alliance chain:

[0057] Job applicant information includes educational background, work experience, skills and expertise, project experience, professional qualification certificates, and interview evaluation records; company job requirements include job title, job responsibilities, job requirements, salary and benefits, and work location.

[0058] Furthermore, in the step of calculating the hash value of key data in the job seeker information and the enterprise job requirement data, determining whether the key data has been tampered with, and outputting the determination result:

[0059] When the judgment result is that the key data has not been tampered with, a request for analyzing the job seeker data and corporate job requirements is triggered;

[0060] When the judgment result is that key data has been tampered with, a tampering tag is generated for the key data, the tampering tag is identified and an early warning is issued.

[0061] In this implementation, job seeker information and company job demand data are first obtained. Job seeker information includes educational background, work experience, skills, project experience, professional qualifications, and interview evaluation records; company job demand data includes job title, job responsibilities, job requirements, salary and benefits, and work location. The job seeker information and company job demand data are then categorized to identify sensitive information requiring encryption. Encryption levels and requirements are identified for each type of data. For example, highly sensitive information such as a job seeker's ID number and contact information requires a strong encryption algorithm.

[0062] Select an appropriate encryption algorithm based on the data's security and performance requirements. Common encryption algorithms include symmetric algorithms (such as AES) and asymmetric algorithms (such as RSA). Symmetric encryption algorithms offer high encryption speeds and are suitable for encrypting large amounts of data; asymmetric encryption algorithms offer high security and are suitable for encrypting secret keys or small amounts of sensitive data. Symmetric encryption algorithms generate a single key for both encryption and decryption. Asymmetric encryption algorithms generate a public and private key pair: the public key is used to encrypt data, and the private key is used to decrypt data.

[0063] Preprocess the data to be encrypted, such as removing irrelevant information and formatting the data, to facilitate encryption. Encrypt the data using the selected encryption algorithm and key. For symmetric encryption, the same key is used for both encryption and decryption. For asymmetric encryption, the public key is used to encrypt the data, and the private key is used for subsequent decryption. Verify the encrypted data to ensure that the encryption process was correct and that the encrypted data cannot be easily decrypted by unauthorized parties. Securely store and manage encryption keys to prevent them from being leaked or lost. For symmetric encryption, keys must be securely distributed to authorized parties; for asymmetric encryption, private keys must be kept strictly confidential. Store the encrypted data in a consortium blockchain. Consortium blockchains provide a decentralized, tamper-proof storage environment, enhancing data security and credibility.

[0064] Calculate the hash value of the key data in the job seeker information and enterprise job demand data stored in the alliance chain (such as job seeker ID, enterprise job ID, educational background, work experience, etc.).

[0065] The calculated hash value is compared with the hash value previously stored on the public blockchain. If the two are consistent, it means that the key data has not been tampered with; if they are inconsistent, it means that the key data has been tampered with.

[0066] When the judgment result is that there is no tampering of the key data, a request for analysis of the job seeker data and corporate job requirements is triggered for subsequent matching and recommendation.

[0067] When the judgment result is that key data has been tampered with, a tampering label is generated for the key data, and an early warning is issued in the system to remind relevant personnel to handle it.

[0068] The tampering judgment formula is as follows:

[0069] Assume that the hash value of the job seeker ID stored on the public blockchain is H public , the hash value of the job seeker ID currently calculated is H current , then the tampering judgment formula is:

[0070]

[0071] Furthermore, in the steps of respectively querying job seeker data and enterprise job requirements, analyzing them, and outputting job seeker tags and job tags:

[0072] S201: Query the encrypted data of the job seeker stored in the alliance chain, decrypt it, and obtain the original data of the job seeker;

[0073] S202: Analyze various data of the job seeker and generate a job seeker tag based on the analysis results;

[0074] S203: Query the encrypted data of enterprise job requirements stored in the alliance chain, decrypt it, and obtain the original data of enterprise job requirements;

[0075] S204: Analyze the enterprise's job requirements and generate job tags based on the analysis results.

[0076] In this embodiment, a connection is made to the consortium chain network, and a specific query interface or tool is used to search for encrypted job seeker data stored within the consortium chain. This data may include the job seeker's basic information, educational background, work experience, and skill certificates. After obtaining the encrypted data, it is decrypted using the decryption key or algorithm used in step S100. The decryption process requires ensuring the security and accuracy of the key to prevent data leakage or decryption failure. After successful decryption, the original form of the job seeker data is obtained, i.e., unencrypted data that can be directly analyzed.

[0077] A comprehensive analysis of the decrypted job applicant data begins with data cleansing to remove duplicate, erroneous, or invalid data. Feature extraction follows, using natural language processing (NLP) techniques to extract keywords from the applicant's skill descriptions. For example, for the skill description "Familiar with Java, Python, and SQL," word segmentation and frequency analysis are performed to extract the skill keywords "Java," "Python," and "SQL."

[0078] Work experience extraction: Extract information such as company name, position title, and working time from work experience. For example, from the work experience "2020-2024, ABC Company, Senior Software Engineer", extract the company name "ABC Company", position "Senior Software Engineer", and working time "2020-2024".

[0079] Use clustering algorithms (such as K-means) to group job applicants based on their skills or work experience. Extract information related to skills and work experience from the decrypted job applicant data. Skill information is usually presented in text form, such as "familiar with Java and Python programming, and understanding of machine learning algorithms"; work experience information includes work time and project experience. Preprocess the skill text, such as word segmentation and stop word removal, to convert the skill text into a quantifiable feature vector. For example, use the bag-of-words model or TF-IDF (term frequency-inverse document frequency) method to convert the skill text into a numerical vector. For work experience, quantify it based on quantitative indicators such as work time and number of projects.

[0080] Because the dimensions and value ranges of different features may vary greatly, for example, some dimensions in the skill vector have a value range between 0 and 1, while the length of work experience may be measured in years and have a large value. In order to eliminate the dimensionality effect and make different features have equal importance in the clustering process, it is necessary to normalize the data. The normalization method is Z-score normalization, and the formula is as follows:

[0081]

[0082] Among them, x is the original data, μ is the mean of the data, σ is the standard deviation of the data, and z is the standardized data.

[0083] Determine the number of clusters K, such as using the silhouette coefficient method: for each data point, calculate its silhouette coefficient. The calculation formula of the silhouette coefficient is:

[0084]

[0085] Where a(i) is the average distance from data point i to all other data points in the same cluster, and b(i) is the minimum average distance from data point i to all data points in different clusters. The silhouette coefficient ranges from -1 to 1, with larger values ​​indicating better clustering.

[0086] Calculate the average silhouette coefficient under different K values, and select the K value with the largest average silhouette coefficient as the number of clusters.

[0087] Initialize the cluster centers, such as by using a random selection method: randomly select K data points from the dataset as the initial cluster centers. For example, in a dataset containing 100 job applicants, if K = 3, then randomly select 3 job applicant data points as the initial cluster centers.

[0088] Assign data points to cluster centers and calculate distances: For each data point, calculate the distance between it and each cluster center. Common distance measurement methods include Euclidean distance, which is as follows:

[0089]

[0090] Among them, x is the data point, μ is the cluster center, n is the dimension of the feature, x j and μ j are the values ​​of the data point and cluster center in the jth dimension respectively.

[0091] Assign the data point to the cluster that is closest to the cluster center. For example, if the data point is closest to cluster center 1, then assign the data point to cluster 1.

[0092] Recalculate the cluster center: For each cluster, calculate the mean of all data points in it and use the mean as the new cluster center. For example, for cluster C i , which contains m data points x1,x2,…,x m , then the new cluster center μ i The calculation formula is:

[0093]

[0094] Repeat the steps of assigning data points to cluster centers and updating cluster centers until the cluster centers no longer change significantly or the preset maximum number of iterations is reached.

[0095] Set a threshold. When the change in cluster center between two adjacent iterations is less than the threshold, the algorithm is considered to have converged and the iteration is stopped.

[0096] Perform feature analysis on each cluster to identify the common skills and work experience characteristics of the job seekers in that cluster. For example, if the job seekers in a cluster mostly have Java and Spring skills, and their work experience is concentrated between 3 and 5 years, then this cluster can be interpreted as a "cluster of mid-level Java developers."

[0097] Generate tags for job seekers based on analysis results for subsequent matching. For example:

[0098] Skill tags: Generate tags based on extracted skill keywords, such as "Java development" and "data analysis".

[0099] Experience tags: Generate tags based on work experience, such as "3 years of project management experience" and "5 years of software development experience".

[0100] Based on the analysis results, corresponding tags are generated for job seekers. These tags may include skill tags (such as "Java development", "data analysis"), experience tags (such as "3 years of project management experience"), and educational background tags (such as "Master's degree").

[0101] Similarly, connect to the consortium blockchain network and use query interfaces or tools to retrieve the stored encrypted data on company job requirements. This data may include job titles, responsibilities, job requirements, salary packages, and other information. Decrypt the encrypted data using the same key or algorithm used to decrypt the job applicant data. After successful decryption, the original data on the company's job requirements is obtained, ready for subsequent analysis.

[0102] Analyze company job requirements and generate job tags based on the results. This analysis can be done in the same way as job seeker data analysis, generating corresponding tags for company positions based on the results. These tags may include job type tags (e.g., "technical position," "management position"), skill requirement tags (e.g., "familiar with Python programming"), and experience requirement tags (e.g., "more than five years of relevant work experience").

[0103] Through the above steps, the entire process of querying and decrypting job seeker data and company job demand data from the alliance chain, and then generating job seeker tags and job tags can be completed. These tags will help in the subsequent matching of job seekers with company positions, improving recruitment efficiency and accuracy.

[0104] Furthermore, in the step of calculating the matching index between the employee and the company's position based on the job seeker tag and the position tag, and generating a recommendation report:

[0105] S301: Obtain job seeker tags and job tags;

[0106] S302: Establishing a matching index range, and dividing the matching degrees into multiple levels according to the matching index range;

[0107] S303: Calculate the matching index between the job seeker and the company's position based on the job seeker tag and the position tag, and output the matching degree;

[0108] S304: Based on the matching degree between multiple job seekers and the position, a job seeker recommendation report for the position is output.

[0109] In this embodiment, the job seeker tag and the position tag are obtained from the step of generating tags in S200. These tags are usually stored in the form of structured data, such as in a database table, where each record corresponds to a tag and its related information.

[0110] Set the matching index value range, such as 0 to 100, where 0 indicates no match at all and 100 indicates a perfect match.

[0111] Based on business needs and actual conditions, the matching index range is divided into multiple matching degrees. For example:

[0112] High match: the matching index is between 80 and 100;

[0113] Moderate match: Match index is between 60 and 79;

[0114] Low match: the match index is between 40 and 59;

[0115] Mismatch: The matching index is between 0 and 39.

[0116] Calculate the matching index between the job seeker and the company's position based on the job seeker's label and position label, and output the matching degree; the label matching degree calculation process is as follows:

[0117] For each label type (such as skills, experience, education, etc.), the matching degree between the job seeker label and the job label is calculated separately.

[0118] The skill tag matching degree calculation is:

[0119] Assume that the skill set required for the position is S required ={s1,s2,…,s m}, the job seeker has a skill set of S candidate ={s1′,s2′,…,s n ′}.

[0120] Calculate the intersection S of two sets match =S required ∩S candidate , that is, the number of skills that job seekers have to meet the job skill requirements.

[0121] Skill tag matching degree M atchskill It can be calculated by the following formula:

[0122]

[0123] Where |Smatch| represents the number of elements in the intersection, and max(|Srequired|,|Scandidate|) represents the maximum number of elements in the two sets. Multiplying by 100 converts the match degree into a percentage.

[0124] Calculation of experience label matching:

[0125] Assume that the required working experience is E required , the applicant's working experience is E candidate .

[0126] Experience label matching experience It can be calculated by the following formula:

[0127]

[0128] Calculation of academic qualification label matching:

[0129] Set a score value for different educational backgrounds, for example: Bachelor's degree is 60 points, Master's degree is 80 points, and Doctoral degree is 100 points. Assume that the educational background score required for the position is L required , the applicant's academic score is L candidate .

[0130] Educational label matching education It can be calculated by the following formula:

[0131]

[0132] Set weights for different tag types, such as skill tag weight W skill =0.5, experience label weight We xperience =0.3, academic qualification label weight W education =0.2, and W skill +W experience +W education =1.

[0133] Comprehensive matching indexMatch total It can be calculated by the following formula:

[0134] Match total =W skill ×Match skill +W experience ×Match experience +Weducation ×Match education

[0135] According to the calculated comprehensive matching index Match total , compare the matching degree range divided before, and output the corresponding matching degree. For example, if Match total =85, then the output is "highly matched".

[0136] Collect the match index and degree of match between multiple job seekers and the position. Sort the candidates by match index from high to low. You can also filter out candidates with a certain degree of match based on business needs, for example, only retaining candidates with a "high match" or "moderate match."

[0137] Generate a recommendation report: The recommendation report can include the following:

[0138] Basic information of the position: position title, job responsibilities, job requirements, etc.

[0139] Job applicant list: lists the sorted job applicants' names, matching index, matching degree, and key tag information (such as main skills, years of work experience, education level, etc.).

[0140] Recommendations: Recommendations are given to different job seekers based on the degree of match and job requirements. For example, job seekers with a “high match” are given priority, while job seekers with a “moderate match” can be further examined.

[0141] In the present invention, job seeker data and enterprise job requirements are first obtained, encrypted and stored in the alliance chain, and the key data hash value is verified through the public blockchain, and the verification result is output; then the job seeker data and enterprise job requirements are queried separately, analyzed, and job seeker tags and job tags are output; finally, based on the job seeker tags and job tags, the matching index between the employee and the enterprise position is calculated, and a recommendation report is generated; by verifying the key data hash value, the feasibility of the data is improved, and the risks caused by data falsification or tampering are reduced, thereby improving the quality of recruitment.

[0142] Corresponding to the aforementioned embodiment of the human resources position matching method, the present application also provides an embodiment of a human resources position matching device.

[0143] Figure 6 This is a block diagram of a human resources position matching device according to an exemplary embodiment. Figure 6 The device may include: a data verification module 401, a job seeker and enterprise position label generation module 402, and a matching index calculation module 403; wherein:

[0144] The data verification module 401 is used to obtain job seeker data and enterprise job requirements, encrypt and store them in the alliance chain, verify the key data hash value through the public blockchain, and output the verification result;

[0145] The job seeker and enterprise position tag generation module 402 is used to query job seeker data and enterprise position requirements, analyze them, and output job seeker tags and position tags;

[0146] The matching index calculation module 403 is used to calculate the matching index between the employee and the company's position based on the job seeker tag and the position tag, and generate a recommendation report.

[0147] In this embodiment, the data verification module 401 obtains job seeker data and enterprise job requirements, encrypts and stores them in the alliance chain, verifies the key data hash value through the public blockchain, and outputs the verification result; the job seeker and enterprise job position label generation module 402 respectively queries the job seeker data and enterprise job requirements, analyzes them, and outputs job seeker labels and job labels; the matching index calculation module 403 calculates the matching index between the employee and the enterprise position based on the job seeker label and job label, and generates a recommendation report; by verifying the key data hash value, the feasibility of the data is improved, and the risks caused by data falsification or tampering are reduced, thereby improving the quality of recruitment.

[0148] Regarding the apparatus in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0149] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to the partial description of the method embodiments. The device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present application scheme. A person of ordinary skill in the art can understand and implement it without paying any creative work.

[0150] Accordingly, the present application also provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned human resource position matching method. Figure 7 As shown in the figure, a hardware structure diagram of a human resource position matching system provided by an embodiment of the present invention is provided for any device with data processing capability, except Figure 7In addition to the processor, memory, and network interface shown, any device with data processing capabilities in which the apparatus in the embodiment is located may also include other hardware, generally based on the actual functions of the device with data processing capabilities, which will not be described in detail.

[0151] Accordingly, the present application also provides a computer-readable storage medium having computer instructions stored thereon, which, when executed by a processor, implement the human resource position matching method as described above. The computer-readable storage medium may be an internal storage unit of any device with data processing capabilities described in any of the aforementioned embodiments, such as a hard disk or memory. The computer-readable storage medium may also be an external storage device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), an SD card, a flash card (Flash Card), etc. equipped on the device. Furthermore, the computer-readable storage medium may also include both an internal storage unit and an external storage device of any device with data processing capabilities. The computer-readable storage medium is used to store the computer program and other programs and data required by any device with data processing capabilities, and may also be used to temporarily store data that has been output or is to be output.

[0152] Those skilled in the art will readily conceive of other embodiments of the present application after considering the specification and practicing the contents disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of this application and include common knowledge or customary techniques in the art that are not disclosed in this application.

[0153] It will be understood that the present application is not limited to the exact construction that has been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof.

Claims

1. A human resources job matching method, characterized in that: The steps include: Obtain job seeker data and company job requirements, encrypt and store them in the alliance chain, verify the key data hash value through the public blockchain, and output the verification results; Query job seeker data and company job requirements respectively, analyze them, and output job seeker tags and job tags; Based on the job seeker tags and job tags, the matching index between the employee and the company's job is calculated, and a recommendation report is generated.

2. The human resource position matching method according to claim 1, characterized in that: In the steps of obtaining job seeker data and company job requirements, encrypting and storing them in the consortium chain, verifying the key data hash value through the public blockchain, and outputting the verification results: Obtain job seeker information and enterprise job demand data respectively, encrypt the data, and store it in the alliance chain; Calculate the hash value of key data in job seeker information and enterprise job demand data, determine whether key data has been tampered with, and output the judgment result.

3. The human resource position matching method according to claim 2, characterized in that: In the steps of obtaining job seeker information and enterprise job demand data, encrypting the data, and storing it in the alliance chain: Job applicant information includes educational background, work experience, skills and expertise, project experience, professional qualification certificates, and interview evaluation records; company job requirements include job title, job responsibilities, job requirements, salary and benefits, and work location.

4. The human resources position matching method according to claim 2, characterized in that: In the steps of calculating the hash value of key data in the job seeker information and the company's job requirement data, determining whether the key data has been tampered with, and outputting the judgment result: When the judgment result is that the key data has not been tampered with, a request for analyzing the job seeker data and corporate job requirements is triggered; When the judgment result is that key data has been tampered with, a tampering tag is generated for the key data, the tampering tag is identified and an early warning is issued.

5. The human resource position matching method according to claim 1, characterized in that: In the steps of querying job seeker data and company job requirements, analyzing them, and outputting job seeker tags and job tags: Query the encrypted data of job seekers stored in the alliance chain, decrypt it, and obtain the original data of the job seekers; Analyze various data of job seekers and generate job seeker tags based on the analysis results.

6. The human resource position matching method according to claim 5, characterized in that: In the steps of querying job seeker data and company job requirements, analyzing them, and outputting job seeker tags and job tags: Query the encrypted data of enterprise job requirements stored in the alliance chain, decrypt it, and obtain the original data of enterprise job requirements; Analyze the company's job requirements and generate job tags based on the analysis results.

7. The human resource position matching method according to claim 1, wherein: In the step of calculating the matching index between the employee and the company's position based on the job seeker tag and position tag, and generating a recommendation report: Get job seeker tags and job tags; Calculate the matching index between job seekers and company positions based on job seeker tags and position tags, and output the matching degree.

8. The human resource position matching method according to claim 7, characterized in that: Before calculating the matching index between job seekers and company positions based on job seeker tags and position tags and outputting the matching degree: A matching index range is established, and multiple matching degrees are divided according to the matching index range.

9. The human resource position matching method according to claim 8, characterized in that: After calculating the matching index between the job seeker and the company's position based on the job seeker tag and position tag, and outputting the matching degree: Based on the matching degree between multiple job seekers and positions, a job seeker recommendation report for the position is output.

10. A human resources position matching device, applied to the human resources position matching method according to claim 1, characterized in that: It includes a data verification module, a job seeker and company position label generation module, and a matching index calculation module; among which: The data verification module is used to obtain job seeker data and enterprise job requirements, encrypt and store them in the alliance chain, verify the key data hash value through the public blockchain, and output the verification result; The job seeker and enterprise position label generation module is used to query job seeker data and enterprise position requirements respectively, analyze them, and output job seeker labels and position labels; The matching index calculation module is used to calculate the matching index between the employee and the enterprise position according to the job seeker tag and the position tag, and generate a recommendation report.

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