CN112541691A - AI-based dynamic job qualification assessment method and system - Google Patents
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Abstract
The invention discloses a dynamic job qualification evaluation method and system based on AI, belonging to the technical field of staff qualification evaluation and comprising the following steps: s1: acquiring employee data; s2: data matching and analyzing; s3: and calculating the scores of the occupational qualification standards. According to the invention, the task type and content of the employee can be automatically analyzed according to the daily task condition of the employee, the capability growth value is calculated, and the method is automatic, intelligent and more accurate in calculation; the staff can work in a daily and regular mode, and the dynamic capacity change can be reflected along with the change of the work content of the staff, so that the growth curve of each staff can be calculated, the situation of the staff can be reflected dynamically, and the staff is worthy of being popularized and used.
Description
Technical Field
The invention relates to the technical field of staff eligibility assessment, in particular to a dynamic eligibility assessment method and system based on AI.
Background
Many companies have built some internal IT systems such as HR systems, OA office systems, project management systems, data collection platforms, etc. But are relatively decentralized and many systems are not built to account for any qualifications. The management of the job qualification and the evaluation of the staff of the company are basically completed by manual maintenance, and the cost is extremely high by combining data of systems such as HR, OA, project management and the like.
The current manual evaluation mode has certain defects, such as: the labor cost is high, the operation of the business process is complex, the calculation method is complex, and the accuracy cannot be guaranteed; the manual evaluation cannot dynamically reflect the ability growth and change conditions of the staff, and the data cannot be updated in time. Therefore, a method and a system for evaluating the dynamic job qualification based on the AI are provided.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: how to solve the defects of the manual evaluation mode and provide a dynamic job qualification evaluation method based on AI.
The invention solves the technical problems through the following technical scheme, and the invention comprises the following steps:
s1: employee data collection
Task scoring data, project contribution, work experience and learning data of the staff are extracted from the corresponding system;
s2: data matching and analysis
Calculating TF-IDF values of the keywords and similarity values with tasks according to keywords of key business activities KPA in a capability map configured in an arbitrary qualification system, daily report contents of the employees and task score values of task completion conditions, and further calculating capability map AbiMap values of the employees;
s3: job qualification criteria score calculation
And calculating the standard score of the employee's competency according to a calculation formula corresponding to the standard score calculation model configuration.
Further, in the step S1, a project contribution value, a work history value, and a learning value are obtained from the project contribution, the work history, and the learning data.
Further, in the step S2, TF-IDF is TF-IDF, where TF is a word frequency indicating a frequency of occurrence of a keyword in the text and IDF is a reverse document frequency.
Further, in the step S2, the similarity value between the keyword and the task is calculated by a cosine similarity algorithm.
Further, in the step S3, the scores of the standard score calculation model are respectively a project contribution value, a work experience value, a learning value, and a capability map AbiMap value.
Furthermore, the configuration module of the standard score calculation model comprises a score type configuration unit, a data viewing unit and a data entry unit; the score type configuration unit is used for adding/deleting score types, changing score types and configuring scoring rules and weights corresponding to the score types; the data viewing unit is used for viewing employee data extracted from the corresponding system; the data entry unit is used for entering the obtained project contribution value, the work experience value, the learning value and the capability map AbiMap value into the standard score calculation model.
Further, in the step S3, the standard score calculation formula is as follows:
Q＝WEV*10％+PCV*30％+LV*10％+AbiMap*50％
wherein WEV represents the work experience value, 10% being its weight; PCV represents the contribution of the item, 30% of its weight; AbiMap represents the ability profile AbiMap value, with 50% as its weight.
The invention also provides a dynamic job qualification evaluation system based on AI, which is used for evaluating the job qualification of the employee by adopting the evaluation method and comprises the following steps:
the data acquisition module is used for extracting task scoring data, project contribution, work experience and learning data of the staff from the corresponding system;
the data matching and analyzing module is used for calculating keywords of key business activity KPA in the competence map, daily report contents of the staff and task score values of task completion conditions, calculating TF-IDF values of the keywords and similarity values of the keywords and the tasks, and further calculating competence map AbiMap values of the staff;
the standard score calculating module is used for calculating the standard score of the employee qualification according to a corresponding calculation formula configured by the standard score calculating model;
the central processing module is used for sending instructions to other modules to complete related actions;
the data acquisition module, the data matching and analyzing module and the standard score calculating module are all electrically connected with the central processing module.
Compared with the prior art, the invention has the following advantages: the AI-based dynamic job qualification assessment method can automatically analyze the task types and contents of the employees according to the daily task conditions of the employees, calculate the capacity growth score, and realize automation, intellectualization and more accurate calculation; the staff can work in a daily and regular mode, and the dynamic capacity change can be reflected along with the change of the work content of the staff, so that the growth curve of each staff can be calculated, the situation of the staff can be reflected dynamically, and the staff is worthy of being popularized and used.
Drawings
FIG. 1 is a schematic flow chart illustrating a method for job qualification according to an embodiment of the present invention;
FIG. 2 is a block diagram of the IQS platform of the second embodiment of the present invention;
fig. 3 is a schematic diagram of a NLP algorithm calculation process in the second embodiment of the present invention.
Detailed Description
The following examples are given for the detailed implementation and specific operation of the present invention, but the scope of the present invention is not limited to the following examples.
Example one
The embodiment provides a technical scheme: an AI-based dynamic due qualification method comprising the steps of:
s1: employee data collection
Task scoring data, project contribution, work experience and learning data of the staff are extracted from the corresponding system;
s2: data matching and analysis
Calculating TF-IDF values of the keywords and similarity values with tasks according to keywords of key business activities KPA in a capability map configured in an arbitrary qualification system, daily report contents of the employees and task score values of task completion conditions, and further calculating capability map AbiMap values of the employees;
s3: job qualification criteria score calculation
And calculating the standard score of the employee's competency according to a calculation formula corresponding to the standard score calculation model configuration.
In step S1, a project contribution value, a work history value, and a learning value are obtained from the project contribution, the work history, and the learning data.
In said step S2, TF-IDF is TF × IDF, where TF is word frequency indicating the frequency of occurrence of keywords in the text and IDF is inverse document frequency.
In step S2, the similarity value between the keyword and the task is calculated by a cosine similarity algorithm.
In step S3, the scores of the standard score calculation model are respectively a project contribution value, a work experience value, a learning value, and a competence map AbiMap value.
The configuration module of the standard score calculation model comprises a score type configuration unit, a data viewing unit and a data entry unit; the score type configuration unit is used for adding/deleting score types, changing score types and configuring scoring rules and weights corresponding to the score types; the data viewing unit is used for viewing employee data extracted from the corresponding system; the data entry unit is used for entering the obtained project contribution value, the work experience value, the learning value and the capability map AbiMap value into the standard score calculation model.
In step S3, the standard score calculation formula is as follows:
Q＝WEV*10％+PCV*30％+LV*10％+AbiMap*50％
wherein WEV represents the work experience value, 10% being its weight; PCV represents the contribution of the item, 30% of its weight; AbiMap represents the ability profile AbiMap value, with 50% as its weight.
The embodiment also provides an AI-based dynamic job qualification assessment system, which is used for assessing the job qualification of an employee by using the assessment method, and the AI-based dynamic job qualification assessment system comprises:
the data acquisition module is used for extracting task scoring data, project contribution, work experience and learning data of the staff from the corresponding system;
the data matching and analyzing module is used for calculating keywords of key business activity KPA in the competence map, daily report contents of the staff and task score values of task completion conditions, calculating TF-IDF values of the keywords and similarity values of the keywords and the tasks, and further calculating competence map AbiMap values of the staff;
the standard score calculating module is used for calculating the standard score of the employee qualification according to a corresponding calculation formula configured by the standard score calculating model;
the central processing module is used for sending instructions to other modules to complete related actions;
the data acquisition module, the data matching and analyzing module and the standard score calculating module are all electrically connected with the central processing module.
Example two
As shown in fig. 2, which is an overall module diagram of an incumbent Qualification system (iqs) platform of this embodiment, the data acquisition module extracts task scoring data, project contribution, work experience, and learning data of an employee from systems such as an OTC, OA, HR, and learning system; calculating the standard score of the employee competency through an NLP algorithm analysis module and an IQS calculation module; and judging the occupational qualification of the employee according to the capacity map in the IQS calculation module and the threshold configuration of the occupational qualification.
As shown in fig. 3, which is a schematic diagram of a computation process of the NLP algorithm adopted by the NLP algorithm analysis module in this embodiment, according to keywords of the key service activity KPA of the capability map configured in the competence system, the daily report content of the staff of the OTC system, and the final score value of the task completion condition, the TF-IDF algorithm is used to compute TF-IDF and cosine similarity values, so as to compute the capability map AbiMap value of the staff.
Introduction of TF-IDF algorithm:
(1) TF is Term Frequency (Term Frequency) indicating the Frequency of occurrence of terms (keywords) in a text;
(2) IDF is Inverse file Frequency (Inverse Document Frequency);
(3) the TF-IDF is actually TF x IDF.
And calculating and comparing the similarity of keywords of the KPA (key performance indicator) of the employee's daily report task, work content and task completion condition configured in the competence graph and the task score value of the employee's task completion condition by matching calculation, thereby calculating the AbiMap value of the employee's competence graph.
"Term Frequency" (Term Frequency, abbreviated TF): if a word is rare but appears in the staff daily newspaper (article) many times, it is likely to reflect the characteristics of the daily newspaper (article), and it is the keyword we need. Expressed in a statistical language, each word is assigned an "importance" weight based on the frequency of the word. This weight is called "Inverse Document Frequency" (IDF for short), and its size is inversely proportional to the degree of commonness of a word.
Multiplying the "word frequency" (TF) and the "inverse document frequency" (IDF) yields a TF-IDF value for a word. The higher the importance of a word to a daily newspaper (article), the larger its TF-IDF value. Therefore, the first words are the keywords of the current daily newspaper (article). From this, keywords of employee daily reports (articles) are found out.
TF-IDF (frequency of word (TF) × Inverse Document Frequency (IDF)
TF-IDF mathematical meaning:
TF: representativeness, the more the occurrence times are, the stronger the representativeness is, and the TF value is larger;
IDF: the universality, which occurs in more places, is stronger, and the IDF value is smaller;
cosine similarity: the closer the cos θ value is to 1, the closer the included angle is to 0 degree, i.e. the more similar the two quantities (keywords in the daily contents and keywords in the capability map configuration) are, which is called cosine similarity.
The ability profile AbiMap score a ═ 1-COS θ task evaluation score.
Table 1 below, a table of itemized scoring rules for standard scores for job eligibility:
TABLE 1 itemized scoring rule Table
| Item | Scoring rules | Full mark | Weight of |
| Working experience value WEV | W*10 | 100 | 10％ |
| Contribution value of item PCV | P/P0 | / | 30％ |
| Learned value LV | 60+(L-L1)/L1*40 | 100 | 10％ |
| Competence map AbiMap | A | 100 | 50％ |
Wherein (W: work experience value, P: yield value in personal project, P0: project yield, L: learning value, L, at this stage1: upper stage learning value, a: capacity map AbiMap score)
Job eligibility criteria score: q WEV + PCV 30% + LV 10% + AbiMap 50%
In the above formula, WEV, PCV, LV and AbiMap have different calculation requirements. Each calculation condition is a result of a relatively complicated operation requiring a large amount of data and a calculation method. The required data is also obtained through various channels, including: the method comprises the following steps of crawler collection, data docking, manual input and the like.
The competency map is one of the core calculations for the dynamic job qualification method.
As shown in table 2 below, is a module of a capability map of the present embodiment.
TABLE 2 parameter Table for modules of a capability map in this embodiment
In the above table, the capability map is a capability map of one of the function level sequences, the product (line) operation module is a module of the capability map in this embodiment, the weight occupied by the product (line) operation module in the capability map AbiMap value is 28%, and the weight occupied by the key service activity KPA of each sub-module and the corresponding keyword are as shown in the above table. This key, i.e. the key for the capability map key business activity KPA configured in the competency system in FIG. 3, is used for subsequent capability map AbiMap value calculations.
And calculating the score of the competence map according to the work task of the staff in the project management system and through an artificial intelligent natural language processing algorithm (NLP algorithm), the similarity of key business activity KPA keywords of the matched competence map and the task score value of the task completion condition of the staff. And (4) summarizing data every day and every month regularly according to task extraction analysis, and calculating the standard score of the employee competency.
The calculation model of the standard score is configurable.
In summary, the method for evaluating the dynamic job qualification based on the AI according to the embodiment can automatically analyze the task type and content of the employee according to the daily task condition of the employee, calculate the capability growth score, and realize automation, intellectualization and more accurate calculation; the staff can work in a daily and regular mode, and the dynamic capacity change can be reflected along with the change of the work content of the staff, so that the growth curve of each staff can be calculated, the situation of the staff can be reflected dynamically, and the staff is worthy of being popularized and used.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (8)
1. An AI-based dynamic due qualification method, comprising the steps of:
s1: employee data collection
Task scoring data, project contribution, work experience and learning data of the staff are extracted from the corresponding system;
s2: data matching and analysis
Calculating TF-IDF values of the keywords and similarity values with tasks according to keywords of key business activities KPA in a capability map configured in an arbitrary qualification system, daily report contents of the employees and task score values of task completion conditions, and further calculating capability map AbiMap values of the employees;
s3: job qualification criteria score calculation
And calculating the standard score of the employee's competency according to a calculation formula corresponding to the standard score calculation model configuration.
2. The AI-based dynamic occupational qualification method of claim 1, wherein: in step S1, a project contribution value, a work history value, and a learning value are obtained from the project contribution, the work history, and the learning data.
3. The AI-based dynamic occupational qualification method of claim 2, wherein: in said step S2, TF-IDF is TF × IDF, where TF is word frequency indicating the frequency of occurrence of keywords in the text and IDF is inverse document frequency.
4. The AI-based dynamic occupational qualification method of claim 3, wherein: in step S2, the similarity value between the keyword and the task is calculated by a cosine similarity algorithm.
5. The AI-based dynamic occupational qualification method of claim 4, wherein: in step S3, the scores of the standard score calculation model are respectively a project contribution value, a work experience value, a learning value, and a competence map AbiMap value.
6. The AI-based dynamic occupational qualification method of claim 5, wherein: the configuration module of the standard score calculation model comprises a score type configuration unit, a data viewing unit and a data entry unit; the score type configuration unit is used for adding/deleting score types, changing score types and configuring scoring rules and weights corresponding to the score types; the data viewing unit is used for viewing employee data extracted from the corresponding system; the data entry unit is used for entering the obtained project contribution value, the work experience value, the learning value and the capability map AbiMap value into the standard score calculation model.
7. The AI-based dynamic occupational qualification method of claim 6, wherein: in step S3, the standard score calculation formula is as follows:
Q＝WEV*10％+PCV*30％+LV*10％+AbiMap*50％
wherein WEV represents the work experience value, 10% being its weight; PCV represents the contribution of the item, 30% of its weight; AbiMap represents the ability profile AbiMap value, with 50% as its weight.
8. An AI-based dynamic due qualification system for assessing due qualifications of an employee using the assessment method according to any one of claims 1 to 7, comprising:
the data acquisition module is used for extracting task scoring data, project contribution, work experience and learning data of the staff from the corresponding system;
the data matching and analyzing module is used for calculating keywords of key business activity KPA in the competence map, daily report contents of the staff and task score values of task completion conditions, calculating TF-IDF values of the keywords and similarity values of the keywords and the tasks, and further calculating competence map AbiMap values of the staff;
the standard score calculating module is used for calculating the standard score of the employee qualification according to a corresponding calculation formula configured by the standard score calculating model;
the central processing module is used for sending instructions to other modules to complete related actions;
the data acquisition module, the data matching and analyzing module and the standard score calculating module are all electrically connected with the central processing module.
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| CN101770709A (en) * | 2008-12-30 | 2010-07-07 | 上海市电力公司 | Career development path and post capacity training authentication system |
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