Method and device for talent evaluation based on multi-level labels

Abstract

The application provides a talent evaluation method and device based on multi-level labels, wherein a person-post matching rule is confirmed based on a first label of an established ability label model; an evaluation questionnaire is called from a data question bank based on a second label of the ability label model; the ability of a person to be evaluated is evaluated based on the evaluation questionnaire, and original data of an evaluation result of the person to be evaluated is obtained; and a post score of the person to be evaluated is obtained based on the original data of the evaluation result, the label weight of the first label and the label weight of the second label in combination with the person-post matching rule. The ability of the person to be evaluated is evaluated through multi-level labels and label weights, the steps are simple, the result can be immediately analyzed, and the matching precision and efficiency of talent evaluation are improved.

Description

Technical Field

[0001] This invention relates to the fields of data processing and assessment technology, and in particular to a talent assessment method and apparatus based on multi-level tags. Background Technology

[0002] Since the company's employees mainly come from campus recruitment in the spring and autumn of each year, the number of new employees is large and they have no work experience. The suitable positions vary, which puts a lot of pressure on the human resources department and makes it easy for employees to be mismatched with positions.

[0003] Existing talent assessment-based job matching methods assess individuals through a pre-set question bank, analyze the results, extract basic personal data, evaluate the abilities of individuals from different dimensions, infer job type scores and job field distribution based on professional ability scores, and generate assessment results by integrating various scores and data characteristics.

[0004] Existing technologies obtain evaluation results by first testing and then analyzing them. The resulting evaluation data is uncontrollable, and there are too many possible outcomes. Data analysis of the evaluation results can easily lead to problems with inaccurate matching, and the process is cumbersome and difficult to quickly analyze the evaluation results.

[0005] Therefore, the cumbersome data processing steps and inaccurate matching in talent assessment are technical problems that urgently need to be solved. Summary of the Invention

[0006] This invention provides a talent assessment method and apparatus based on multi-level tags to address the aforementioned deficiencies in the prior art. It enables the assessment of the abilities of candidates through multi-level tags and tag weights, with simple steps and immediate results analysis, thereby improving the matching accuracy and efficiency of talent assessment.

[0007] This invention provides a talent assessment method based on multi-level tags, comprising:

[0008] Based on the secondary tags of the aforementioned ability tag model, the assessment questionnaire is retrieved from the question bank.

[0009] The abilities of the individuals to be assessed are evaluated based on the assessment questionnaire, and the raw assessment results data of the individuals to be assessed are obtained.

[0010] Based on the person-job matching rules, the job score of the person to be evaluated is obtained by combining the original data of the assessment results, the label weights of the first-level labels and the label weights of the second-level labels.

[0011] According to a multi-level tag-based talent assessment method provided by the present invention, the step of assessing the abilities of the person to be assessed based on the assessment questionnaire to obtain the raw data of the assessment results of the person to be assessed includes:

[0012] Set a time limit for each assessment question in the assessment questionnaire;

[0013] The abilities of the person to be assessed are evaluated based on the assessment questions and the corresponding answering time.

[0014] When the allotted time for answering questions ends, the assessment will automatically exit and the raw assessment results data of the person being assessed will be obtained.

[0015] According to a talent assessment method based on multi-level tags provided by the present invention, the method for confirming job-person matching rules based on first-level tags of an established competency tag model includes:

[0016] The person-job matching rules are confirmed based on the company's pre-defined job positions and the primary tags.

[0017] The primary label is determined based on the talent requirements of the pre-defined positions.

[0018] According to a talent assessment method based on multi-level tags provided by the present invention, the method for establishing the ability tag model includes:

[0019] The primary tags are determined based on the talent requirements of the pre-defined positions, and the tag weights of the primary tags are also determined.

[0020] The primary tags are divided into secondary tags with multiple dimensions, and the tag weights of the secondary tags are determined.

[0021] The capability tag model is established based on the primary and secondary tags, the tag weight of the primary tag, and the tag weight of the secondary tag.

[0022] According to a talent assessment method based on multi-level tags provided by the present invention, the step of dividing the primary tags into secondary tags of multiple dimensions and confirming the tag weights of the secondary tags includes:

[0023] Based on market demand, key factors are extracted from the competency requirements of corporate recruitment positions;

[0024] Based on the division of the key factors into multiple dimensions, the primary tags are used to obtain secondary tags with multiple dimensions.

[0025] Based on the frequency of occurrence of the key factors and the salary data of the job postings in the company, the tag weight of the primary tag is determined.

[0026] According to a talent assessment method based on multi-level tags provided by the present invention, the step of dividing the primary tags into secondary tags of multiple dimensions and confirming the tag weights of the secondary tags includes:

[0027] Extract feature variables from the company's talent database;

[0028] The primary labels are divided into multiple dimensions based on the feature variables and the company's historical rating records to obtain secondary labels in multiple dimensions.

[0029] Based on the data update of the talent database, the tag weights of the secondary tags are updated and confirmed.

[0030] According to a talent assessment method based on multi-level tags provided by the present invention, the step of dividing the primary tags into secondary tags of multiple dimensions and confirming the tag weights of the secondary tags includes:

[0031] Once the enterprise confirms the job requirements for the pre-defined positions, the primary tags are automatically divided into multiple dimensions based on the enterprise's needs, resulting in secondary tags with multiple dimensions.

[0032] Based on the enterprise's needs, the tag weight of the secondary tag is automatically set.

[0033] This invention also provides a multi-level tag talent assessment device, comprising:

[0034] The rule confirmation module is used to confirm the person-job matching rules based on the first-level tags of the established competency tag model;

[0035] The calling module is used to call assessment questionnaires from the data question bank based on the secondary tags of the ability tag model;

[0036] The assessment module is used to assess the abilities of the person to be assessed based on the assessment questionnaire, and obtain the raw data of the assessment results of the person to be assessed.

[0037] The scoring module is used to combine the person-job matching rules and, based on the original data of the assessment results, the label weights of the first-level labels, and the label weights of the second-level labels, to obtain the job score of the person to be assessed.

[0038] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the talent assessment device method with multi-level tags as described above.

[0039] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the talent assessment device method with multi-level tags as described above.

[0040] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the talent assessment device and method with multi-level tags as described above.

[0041] The present invention provides a multi-level tag-based talent assessment method and apparatus. It first confirms the person-job matching rules based on the first-level tags of an established competency tag model. Then, based on the second-level tags of the competency tag model, it retrieves assessment questionnaires from a database to further assess the abilities of the candidates, obtaining raw assessment data. Combining the person-job matching rules with the raw assessment data and the preset tag weights of the first and second-level tags, it obtains the candidate's job score. This invention assesses the abilities of candidates through multi-level tags and tag weights, with simple steps and immediate results analysis, improving the matching accuracy and efficiency of talent assessment. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0043] Figure 1 This is one of the flowcharts of the multi-level tag talent assessment method provided by the present invention;

[0044] Figure 2 This is the second flowchart of the multi-level tag talent assessment method provided by the present invention;

[0045] Figure 3 This is a schematic diagram of the structure of the multi-level tag talent assessment device provided by the present invention;

[0046] Figure 4 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0048] Reference Figure 1 The talent assessment method based on multi-level tags provided by this invention includes the following steps:

[0049] Step 110: Confirm the job matching rules based on the first-level tags of the established competency tag model;

[0050] Step 120: Based on the secondary tags of the ability tag model, retrieve the assessment questionnaire from the question bank;

[0051] Step 130: Assess the abilities of the person to be assessed based on the assessment questionnaire to obtain the raw assessment results data of the person to be assessed;

[0052] Step 140: Combining the person-job matching rules, based on the original data of the assessment results, the label weights of the first-level labels, and the label weights of the second-level labels, obtain the job score of the person to be assessed.

[0053] First, it should be noted that the executing entity of the multi-level tag-based talent assessment method provided by this invention can be an electronic device, a component within an electronic device, an integrated circuit, or a chip. This electronic device can be a mobile electronic device or a non-mobile electronic device. For example, a mobile electronic device can be a mobile phone, tablet computer, laptop computer, PDA, ultra-mobile personal computer (UMPC), netbook, or personal digital assistant (PDA), etc., while a non-mobile electronic device can be a server, network attached storage (NAS), or personal computer (PC), etc. This invention does not impose specific limitations. The following describes the steps of this invention in detail by using a computer to execute the multi-level tag-based talent assessment method provided by this invention.

[0054] In step 110, the person-job matching rules are first determined based on the primary tags of the established competency tag model. The primary tags represent the general direction of the talent needs for the pre-defined positions of the enterprise or company, such as the technical personnel, clerical personnel, logistics personnel, marketing personnel, and financial personnel required by the company.

[0055] Then, the pre-set positions of enterprises or companies are bound to the personnel required for those positions, that is, a matching relationship is established, and a person-job matching rule is generated.

[0056] In step 220, questions are randomly selected from the system's question bank based on the secondary tags of the competency tag model to form a unique assessment questionnaire, which is then used to assess the competency of the candidates.

[0057] It should be noted that the secondary labels in step 220 are further subdivisions of the primary labels in step 210. For example, the primary label "technical personnel" can be specifically divided into R&D personnel, testing personnel, hardware development personnel, software development personnel, and product personnel, etc. The primary label "marketing personnel" can be specifically divided into marketing managers, telesales personnel, business secretaries, etc.

[0058] Based on the specific job requirements corresponding to the secondary labels, test questionnaires are selected from the data question bank, meaning different secondary labels correspond to different test questionnaires.

[0059] It should be added that the question bank is established by collecting and storing large amounts of data on personnel recruitment and assessment questions. In actual personnel assessment, questions are randomly selected from the question bank using secondary tags to form a unique assessment questionnaire, thereby ensuring that the assessment questionnaire is not unique, reducing the randomness of the assessment and increasing its authenticity.

[0060] Furthermore, in step 130, the abilities of the person to be assessed are evaluated based on the assessment questionnaire retrieved from the data question bank, and the raw data of the assessment results of the person to be assessed are obtained.

[0061] Different individuals can be assessed using a questionnaire composed of selected test items. Because the test items are randomly selected, the random and non-unique nature of the questionnaire reduces the randomness of the assessment and increases its authenticity. Raw assessment data can be obtained immediately after the assessors complete the assessment.

[0062] Finally, in step 140, the job score of the person to be evaluated is obtained by combining the job matching rules obtained in step 110 with the label weights of the first-level labels in step 110, the label weights of the second-level labels in step 120, and the raw data of the evaluation results in step 130.

[0063] The calculation formula for step 140 is shown in the following formula (1):

[0064]

[0065] Among them, S n Indicates: Job score, M i Indicates: the tag weight of the first-level tag; B i Represents: Raw data of the evaluation results, N i This indicates the tag weight of the second-level tag.

[0066] In the actual assessment process of enterprises or companies, different job matching degrees can be sorted from high to low according to the job score, so as to obtain the most suitable job for the person being assessed and quickly and accurately assign positions to newly hired employees of the demanding unit.

[0067] The talent assessment method based on multi-level tags provided in this invention confirms the person-job matching rules based on the first-level tags of an established competency tag model. Then, based on the second-level tags of the competency tag model, it retrieves assessment questionnaires from a question bank and further assesses the abilities of the candidates based on these questionnaires, obtaining the raw assessment results data of the candidates. Combining the person-job matching rules with the raw assessment results data and the preset tag weights of the first-level and second-level tags, the job score of the candidates is obtained. This invention assesses the abilities of candidates through multi-level tags and tag weights, with simple steps and immediate results analysis, improving the matching accuracy and efficiency of talent assessment.

[0068] Based on the above embodiments, the step of assessing the abilities of the person to be assessed based on the assessment questionnaire to obtain the raw data of the assessment results of the person to be assessed includes:

[0069] Set a time limit for each assessment question in the assessment questionnaire;

[0070] The abilities of the person to be assessed are evaluated based on the assessment questions and the corresponding answering time.

[0071] When the allotted time for answering questions ends, the assessment will automatically exit and the raw assessment results data of the person being assessed will be obtained.

[0072] In this embodiment, a time limit is set for each assessment question in the assessment questionnaire. Under the time limit, the assessment is conducted on the candidates through each assessment question. When the time limit for each assessment question ends, the assessment is automatically terminated, which means that the assessment time for this question has been used up. At this time, the assessment will be automatically terminated, thereby reducing the assessment time and improving the assessment efficiency.

[0073] Then, the scores of all assessment questions are tallied and summarized to obtain the final score of the assessment questionnaire.

[0074] The talent assessment method based on multi-level tags provided in this invention assesses the abilities of the candidates within a limited time frame for answering questions, thereby completing the assessment within a certain period of time and improving assessment efficiency by reducing assessment time.

[0075] Based on the above embodiments, the first-level label confirmation rule for job matching based on the established competency label model includes:

[0076] The person-job matching rules are confirmed based on the company's pre-defined job positions and the primary tags.

[0077] The primary label is determined based on the talent requirements of the pre-defined positions.

[0078] This embodiment binds the pre-set positions of an enterprise or company with the personnel required for those positions, that is, it establishes a matching relationship and generates personnel-position matching rules.

[0079] Based on the above embodiments, the method for establishing the capability labeling model includes:

[0080] The primary tags are determined based on the talent requirements of the pre-defined positions, and the tag weights of the primary tags are also determined.

[0081] The primary tags are divided into secondary tags with multiple dimensions, and the tag weights of the secondary tags are determined.

[0082] The capability tag model is established based on the primary and secondary tags, the tag weight of the primary tag, and the tag weight of the secondary tag.

[0083] This embodiment provides the process of establishing a capability tag model. First, it is necessary to confirm the primary tags through talent needs. Then, the tag weight of the primary tags is set according to the specific talent needs. It can be set to 0.8 or 0.2, or it can be adjusted according to the actual situation. No further restrictions are made here.

[0084] Then, the tags are divided according to multiple dimensions to obtain multiple secondary tags. The tag weight of the secondary tags can be set according to the specific needs of the secondary tags and other factors. It can be set to 0.3, 0.2, 0.2, 0.1, 0.1, 0.1, or adjusted according to the actual situation. No further restrictions are made here.

[0085] Finally, a capability tag model is established by using first-level tags, second-level tags to form multi-level tags, and the tag weights corresponding to each tag.

[0086] It should be noted that the methods for establishing the capability label model mentioned in the above embodiments may include market demand modeling, talent pool modeling, and custom modeling. The three modeling methods will be described below through three different embodiments.

[0087] Based on the above embodiments, the step of dividing the primary tag into secondary tags with multiple dimensions and confirming the tag weight of the secondary tags includes:

[0088] Based on market demand, key factors are extracted from the competency requirements of corporate recruitment positions;

[0089] Based on the division of the key factors into multiple dimensions, the primary tags are used to obtain secondary tags with multiple dimensions.

[0090] Based on the frequency of occurrence of the key factors and the salary data of the job postings in the company, the tag weight of the primary tag is determined.

[0091] This embodiment provides the process of establishing a capability label model based on market demand modeling.

[0092] In this embodiment, the system can extract key factors by analyzing the competency requirements of a large number of enterprise job postings, and then use these key factors to divide the primary tags as secondary tags for multiple dimensions.

[0093] Then, the label weights of the secondary labels are determined based on the frequency of occurrence of key factors and factors such as job salary. Furthermore, the model is continuously updated and improved through recruitment data, and the label weight values ​​are constantly adjusted to approximate actual market demand.

[0094] Based on the above embodiments, the step of dividing the primary tag into secondary tags with multiple dimensions and confirming the tag weight of the secondary tags includes:

[0095] Extract feature variables from the company's talent database;

[0096] The primary labels are divided into multiple dimensions based on the feature variables and the company's historical rating records to obtain secondary labels in multiple dimensions.

[0097] Based on the data update of the talent database, the tag weights of the secondary tags are updated and confirmed.

[0098] Specifically, this embodiment provides a process for establishing a capability tag model based on the talent pool modeling method.

[0099] In this embodiment, since the system has a basic talent pool, the system data can be expanded by importing the company's talent pool if a company's talent pool is available. Key talent characteristic variables can be extracted from the talent pool, combined with the company's past scoring records, to obtain secondary labels and set label weights.

[0100] The specific implementation involves first extracting feature variables from the company's talent database. Then, based on these feature variables and the company's historical rating records, primary tags are divided into multiple dimensions to obtain secondary tags. Finally, the weights of the secondary tags are updated and confirmed using data from the talent database. Through continuous improvement and self-adjustment of the company's talent pool, it becomes more closely aligned with the company's specific needs.

[0101] Based on the above embodiments, the step of dividing the primary tag into secondary tags with multiple dimensions and confirming the tag weight of the secondary tags includes:

[0102] Once the enterprise confirms the job requirements for the pre-defined positions, the primary tags are automatically divided into multiple dimensions based on the enterprise's needs, resulting in secondary tags with multiple dimensions.

[0103] Based on the enterprise's needs, the tag weight of the secondary tag is automatically set.

[0104] Specifically, this embodiment provides a process for establishing a capability label model based on a custom modeling method.

[0105] In practice, some enterprises or companies have a high degree of clarity regarding their job requirements. They can design their own second- and third-level tags and corresponding weights within the system and bind them to the corresponding job positions. The system will automatically generate a basic model by verifying the validity of the tags.

[0106] When an enterprise or company clearly confirms the job requirements for a pre-defined position, the primary tags are automatically divided into multiple dimensions based on the enterprise's needs, resulting in secondary tags with multiple dimensions. Then, based on the enterprise's needs, the tag weights of the secondary tags are automatically set.

[0107] The talent assessment method based on multi-level tags provided in this invention establishes a competency tag model through various modeling methods. This model can comprehensively and realistically reflect the competencies required for the job position, and determine the competency of the recruited personnel based on various competencies. Furthermore, it is not only based on the standards for personnel selection by a single company, but also allows employers to freely choose from multiple modeling methods in different scenarios.

[0108] Reference Figure 2 , Figure 2 This is a complete flowchart of the talent assessment method based on multi-level tags provided by the present invention, including:

[0109] Step 210: Define basic primary tags based on company job requirements, refine the primary tags into six dimensions to form secondary tags, bind the pre-defined job positions to the primary tags, and generate person-job matching rules.

[0110] Step 220: Randomly select questions from the system's question bank using secondary tags to form a personalized assessment questionnaire, limit the time for each question, and ensure data authenticity by exiting and submitting, thereby obtaining the raw assessment data;

[0111] Step 230: The system calculates scores for the raw data of the evaluators based on the preset weights of the first-level labels and the preset weights of the second-level labels, calculates the job scores through the person-job matching rules, and determines the suitable jobs based on the scoring standards.

[0112] The following describes the talent assessment device based on multi-level tags provided by the present invention. The talent assessment device based on multi-level tags described below can be referred to in correspondence with the talent assessment method based on multi-level tags described above.

[0113] Reference Figure 3 The present invention also provides a talent assessment device based on multi-level tags, comprising:

[0114] Rule confirmation module 310 is used to confirm the person-job matching rules based on the first-level tags of the established capability tag model;

[0115] The calling module 320 is used to call the assessment questionnaire from the data question bank based on the secondary tags of the ability tag model;

[0116] The assessment module 330 is used to assess the abilities of the person to be assessed based on the assessment questionnaire, and obtain the raw data of the assessment results of the person to be assessed.

[0117] The scoring module 340 is used to combine the person-job matching rules and, based on the original data of the assessment results, the label weights of the first-level labels, and the label weights of the second-level labels, to obtain the job score of the person to be assessed.

[0118] In the rule confirmation module 310, the person-job matching rules are first determined based on the primary tags of the established capability tag model. The primary tags represent the general direction of the talent needs for the pre-defined positions of the enterprise or company, such as the technical personnel, clerical personnel, logistics personnel, marketing personnel, and financial personnel required by the company.

[0119] Then, the pre-set positions of enterprises or companies are bound to the personnel required for those positions, that is, a matching relationship is established, and a person-job matching rule is generated.

[0120] In module 320, questions are randomly selected from the system's question bank based on the secondary tags of the competency tag model to form a unique assessment questionnaire, which is then used to assess the competency of the candidates.

[0121] It should be noted that the secondary tags in module 320 are further subdivisions of the primary tags in rule confirmation module 310. For example, the primary tag "technical personnel" can be specifically divided into R&D personnel, testing personnel, hardware development personnel, software development personnel, and product personnel, etc. The primary tag "marketing personnel" can be specifically divided into marketing managers, telesales personnel, business secretaries, etc.

[0122] Based on the specific job requirements corresponding to the secondary labels, test questionnaires are selected from the data question bank, meaning different secondary labels correspond to different test questionnaires.

[0123] It should be added that the question bank is established by collecting and storing large amounts of data on personnel recruitment and assessment questions. In actual personnel assessment, questions are randomly selected from the question bank using secondary tags to form a unique assessment questionnaire, thereby ensuring that the assessment questionnaire is not unique, reducing the randomness of the assessment and increasing its authenticity.

[0124] Furthermore, in the assessment module 330, the ability of the person to be assessed is assessed based on the assessment questionnaire retrieved from the data question bank, and the raw data of the assessment results of the person to be assessed is obtained.

[0125] Different individuals can be assessed using a questionnaire composed of selected test items. Because the test items are randomly selected, the random and non-unique nature of the questionnaire reduces the randomness of the assessment and increases its authenticity. Raw assessment data can be obtained immediately after the assessors complete the assessment.

[0126] Finally, in the score acquisition module 340, the job matching rules obtained in the rule confirmation module 310 are combined with the label weights of the first-level labels in the rule confirmation module 310, the label weights of the second-level labels in the rule confirmation module 310, and the raw data of the assessment results in the assessment module 330 to calculate and obtain the job score of the person to be assessed.

[0127] The calculation formula for the score acquisition module 340 is shown in the following formula (1):

[0128]

[0129] Among them, S n Indicates: Job score, M i Indicates: the tag weight of the first-level tag; B iRepresents: Raw data of the evaluation results, N i This indicates the tag weight of the second-level tag.

[0130] In the actual assessment process of enterprises or companies, different job matching degrees can be sorted from high to low according to the job score, so as to obtain the most suitable job for the person being assessed and quickly and accurately assign positions to newly hired employees of the demanding unit.

[0131] The talent assessment device based on multi-level tags provided by this invention confirms the person-job matching rules based on the first-level tags of an established competency tag model. Then, based on the second-level tags of the competency tag model, it retrieves assessment questionnaires from a database to further assess the abilities of the candidates, obtaining the raw assessment results data. Combining the person-job matching rules with the raw assessment results data and the preset tag weights of the first-level and second-level tags, the device obtains the candidate's job score. This invention assesses the abilities of candidates through multi-level tags and tag weights, with simple steps and immediate results analysis, improving the matching accuracy and efficiency of talent assessment.

[0132] Based on the above embodiments, the evaluation module is specifically used for:

[0133] Set a time limit for each assessment question in the assessment questionnaire;

[0134] The abilities of the individuals to be assessed are tested based on the assessment questions and the corresponding answering time.

[0135] When the allotted time for answering questions ends, the test will automatically exit and the raw data of the evaluation results for the person being evaluated will be obtained.

[0136] Based on the above embodiments, the rule confirmation module is specifically used for:

[0137] The person-job matching rules are confirmed based on the company's pre-defined job positions and the primary tags.

[0138] The primary label is determined based on the talent requirements of the pre-defined positions.

[0139] Based on the above embodiments, the method for establishing the capability labeling model includes:

[0140] The primary tag confirmation unit is used to confirm the primary tag based on the talent requirements of the preset positions and to confirm the tag weight of the primary tag.

[0141] The secondary tag segmentation unit is used to divide the primary tag into secondary tags with multiple dimensions and to determine the tag weight of the secondary tags;

[0142] The model building unit is used to build the capability label model based on the first-level label and the second-level label, the label weight of the first-level label and the label weight of the second-level label.

[0143] Based on the above embodiments, the secondary label segmentation unit is specifically used for:

[0144] Based on market demand, key factors are extracted from the competency requirements of corporate recruitment positions;

[0145] Based on the division of the key factors into multiple dimensions, the primary tags are used to obtain secondary tags with multiple dimensions.

[0146] Based on the frequency of occurrence of the key factors and the salary data of the job postings in the company, the tag weight of the primary tag is determined.

[0147] Based on the above embodiments, the secondary label segmentation unit is further used for:

[0148] Extract feature variables from the company's talent database;

[0149] The primary labels are divided into multiple dimensions based on the feature variables and the company's historical rating records to obtain secondary labels in multiple dimensions.

[0150] Based on the data update of the talent database, the tag weights of the secondary tags are updated and confirmed.

[0151] Based on the above embodiments, the secondary label segmentation unit is further used for:

[0152] Once the enterprise confirms the job requirements for the pre-defined positions, the primary tags are automatically divided into multiple dimensions based on the enterprise's needs, resulting in secondary tags with multiple dimensions.

[0153] Based on the enterprise's needs, the tag weight of the secondary tag is automatically set.

[0154] Figure 4 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 4 As shown, the electronic device may include: a processor 410, a communication interface 420, a memory 430, and a communication bus 440, wherein the processor 410, the communication interface 420, and the memory 430 communicate with each other through the communication bus 440. The processor 410 can call logical instructions in the memory 430 to execute a talent assessment method based on multi-level tags, which includes:

[0155] The first-level tags of the established competency tag model are used to confirm the person-job matching rules.

[0156] Based on the secondary tags of the aforementioned ability tag model, the assessment questionnaire is retrieved from the question bank.

[0157] The assessment questionnaire is used to test the abilities of the individuals to be assessed, and the raw assessment results data of the individuals to be assessed are obtained.

[0158] Based on the person-job matching rules, the job score of the person to be evaluated is obtained by combining the original data of the assessment results, the label weights of the first-level labels and the label weights of the second-level labels.

[0159] Furthermore, the logical instructions in the aforementioned memory 430 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0160] On the other hand, the present invention also provides a computer program product, the computer program product comprising a computer program that can be stored on a non-transitory computer-readable storage medium, wherein when the computer program is executed by a processor, the computer is able to execute the talent assessment method based on multi-level tags provided by the above methods, the method comprising:

[0161] The first-level tags of the established competency tag model are used to confirm the person-job matching rules.

[0162] Based on the secondary tags of the aforementioned ability tag model, the assessment questionnaire is retrieved from the question bank.

[0163] The assessment questionnaire is used to test the abilities of the individuals to be assessed, and the raw assessment results data of the individuals to be assessed are obtained.

[0164] Based on the person-job matching rules, the job score of the person to be evaluated is obtained by combining the original data of the assessment results, the label weights of the first-level labels and the label weights of the second-level labels.

[0165] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the talent assessment method based on multi-level tags provided by the above methods, the method comprising:

[0166] The first-level tags of the established competency tag model are used to confirm the person-job matching rules.

[0167] Based on the secondary tags of the aforementioned ability tag model, the assessment questionnaire is retrieved from the question bank.

[0168] The assessment questionnaire is used to test the abilities of the individuals to be assessed, and the raw assessment results data of the individuals to be assessed are obtained.

[0169] Based on the person-job matching rules, the job score of the person to be evaluated is obtained by combining the original data of the assessment results, the label weights of the first-level labels and the label weights of the second-level labels.

[0170] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0171] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0172] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A talent assessment method based on multi-level tags, characterized in that, include: The competency tag model is used to determine the person-job matching rules based on the primary tags. The primary tags represent the talent needs of the company's pre-defined positions, and the person-job matching rules are the binding and matching relationships between the company's pre-defined positions and the primary tags. The method for establishing the competency tag model includes: The primary tags are determined based on the talent requirements of the pre-defined positions, and the tag weights of the primary tags are also determined. The primary tags are divided into secondary tags with multiple dimensions, and the tag weights of the secondary tags are determined. Based on the primary and secondary tags, the tag weights of the primary tags and the tag weights of the secondary tags, the capability tag model is established. Based on the secondary tags of the ability tag model, assessment questionnaires are retrieved from the question bank; wherein, the secondary tags are a further subdivision of the primary tags, and different secondary tags correspond to different assessment questionnaires. Questions are randomly selected from the question bank using the secondary tags to form a unique assessment questionnaire, so that the assessment questionnaire is not unique. The abilities of the individuals to be assessed are evaluated based on the assessment questionnaire, and the raw assessment results data of the individuals to be assessed are obtained. Based on the person-job matching rules, the original data of the assessment results, the label weights of the first-level labels, and the label weights of the second-level labels, the job score of the person to be assessed is obtained; the job score of the person to be assessed is calculated using the following formula: ； in, This indicates the score for the stated position. This indicates the tag weight of the first-level tag. This represents the raw data of the evaluation results. This indicates the label weight of the secondary label; Based on the job scores, different job matching degrees are sorted from high to low to obtain the most suitable job for the person to be evaluated.

2. The talent assessment method based on multi-level tags according to claim 1, characterized in that, The assessment of the candidate's abilities based on the assessment questionnaire, to obtain the raw data of the assessment results for the candidate, includes: Set a time limit for each assessment question in the assessment questionnaire; The abilities of the person to be assessed are evaluated based on the assessment questions and the corresponding answering time. When the allotted time for answering questions ends, the assessment will automatically exit and the raw assessment results data of the person being assessed will be obtained.

3. The talent assessment method based on multi-level tags according to claim 1, characterized in that, The step of dividing the primary tag into secondary tags with multiple dimensions and determining the tag weight of the secondary tags includes: Based on market demand, key factors are extracted from the competency requirements of corporate recruitment positions; Based on the division of the key factors into multiple dimensions, the primary tags are used to obtain secondary tags with multiple dimensions. Based on the frequency of occurrence of the key factors and the salary data of the job postings in the company, the tag weight of the primary tag is determined.

4. The talent assessment method based on multi-level tags according to claim 1, characterized in that, The step of dividing the primary tag into secondary tags with multiple dimensions and determining the tag weight of the secondary tags includes: Extract feature variables from the company's talent database; The primary labels are divided into multiple dimensions based on the feature variables and the company's historical rating records to obtain secondary labels in multiple dimensions. Based on the data update of the talent database, the tag weights of the secondary tags are updated and confirmed.

5. The talent assessment method based on multi-level tags according to claim 1, characterized in that, The step of dividing the primary tag into secondary tags with multiple dimensions and determining the tag weight of the secondary tags includes: Once the enterprise confirms the job requirements for the pre-defined positions, the primary tags are automatically divided into multiple dimensions based on the enterprise's needs, resulting in secondary tags with multiple dimensions. Based on the enterprise's needs, the tag weight of the secondary tag is automatically set.

6. A talent assessment device based on multi-level tags, characterized in that, include: The rule confirmation module is used to confirm the person-job matching rules based on the first-level tags of the established competency tag model. The first-level tags represent the talent demand direction of the enterprise's pre-defined positions, and the person-job matching rules represent the binding and matching relationship between the enterprise's pre-defined positions and the first-level tags. The method for establishing the competency tag model includes: confirming the first-level tags according to the talent demand of the pre-defined positions, and confirming the tag weights of the first-level tags; dividing the first-level tags into multiple dimensions of second-level tags, and confirming the tag weights of the second-level tags; and establishing the competency tag model based on the first-level tags, the second-level tags, the tag weights of the first-level tags, and the tag weights of the second-level tags. The calling module is used to call assessment questionnaires from the question bank based on the secondary tags of the ability tag model; wherein, the secondary tags are a further subdivision of the primary tags, and different secondary tags correspond to different assessment questionnaires. Questions are randomly selected from the question bank using the secondary tags to form a unique assessment questionnaire, so that the assessment questionnaire is not unique. The assessment module is used to assess the abilities of the person to be assessed based on the assessment questionnaire, and obtain the raw data of the assessment results of the person to be assessed. The scoring module is used to combine the person-job matching rules with the raw data of the assessment results, the tag weights of the first-level tags, and the tag weights of the second-level tags to obtain the job score of the person to be assessed; the job score of the person to be assessed is calculated using the following formula: ； in, This indicates the score for the stated position. This indicates the tag weight of the first-level tag. This represents the raw data of the evaluation results. The label weight of the secondary label is indicated; based on the job score, different job matching degrees are sorted from high to low to obtain the most suitable job for the person to be evaluated.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the talent assessment method based on multi-level tags as described in any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the talent assessment method based on multi-level tags as described in any one of claims 1 to 5.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the talent assessment method based on multi-level tags as described in any one of claims 1 to 5.

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