CN117893098A - High-end industry talent introduction effect evaluation method and system - Google Patents

High-end industry talent introduction effect evaluation method and system Download PDF

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CN117893098A
CN117893098A CN202410248889.9A CN202410248889A CN117893098A CN 117893098 A CN117893098 A CN 117893098A CN 202410248889 A CN202410248889 A CN 202410248889A CN 117893098 A CN117893098 A CN 117893098A
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evaluation
level
dimension
determining
key index
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王亚晨
冯鹏飞
杜雪萍
李博文
于伟伟
康洋鸣
万斯斯
李世杰
闫思雨
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Institute Of Geographical Sciences Henan Academy Of Sciences
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Institute Of Geographical Sciences Henan Academy Of Sciences
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Abstract

A talent guiding effect evaluation method and system for high-end industry relate to the technical field of data evaluation. In the method, a first-level evaluation dimension is determined based on an evaluation target requirement; determining a plurality of first-level evaluation indexes based on the first-level evaluation dimension; determining a first evaluation score corresponding to a person to be evaluated based on the first-level evaluation indexes; when the first evaluation score is larger than a preset first evaluation threshold, determining a second evaluation dimension based on the first evaluation dimension; based on the second-level evaluation dimension, obtaining a plurality of second-level evaluation indexes; determining a second evaluation score corresponding to the person to be evaluated based on the plurality of second-level evaluation indexes; based on the first evaluation score and the second evaluation score, obtaining a comprehensive evaluation score corresponding to the person to be evaluated; and obtaining a talent introduction effect evaluation result of the high-end industry based on the comprehensive evaluation score. By implementing the technical scheme, the comprehensiveness and accuracy of the talent guiding effect evaluation can be effectively improved.

Description

High-end industry talent introduction effect evaluation method and system
Technical Field
The application relates to the technical field of data evaluation, in particular to a talent guiding effect evaluation method and system for high-end industries.
Background
Talent reserves are one of the key factors for the development of urban economic culture. The talent guiding effect evaluation result of the high-end industry is one of important guiding factors for adjusting talent reservation strategy. The talent reserve strategy is adjusted according to the evaluation result of talent guiding effect of high-end industry, so that blindness of talent guiding can be reduced. The current talent guiding effect evaluation method for high-end industries generally evaluates talent guiding effects according to the development condition of high-end industrial enterprises for talent guiding. However, this method can only reflect to some extent the contribution of talent introduction to the high-end industry, and cannot comprehensively and accurately evaluate the effect of talent introduction.
Therefore, how to accurately and comprehensively evaluate talent guiding effects becomes a technical problem to be solved.
Disclosure of Invention
The method and the system for evaluating talent guiding effect of high-end industry can effectively improve the comprehensiveness and accuracy of talent guiding effect evaluation.
In a first aspect, the present application provides a method for evaluating talent guiding effects of high-end industries, the method comprising: determining a first-level evaluation dimension based on the evaluation target requirement; determining a plurality of primary evaluation indexes based on the primary evaluation dimension; determining a first evaluation score corresponding to the person to be evaluated based on the plurality of first-level evaluation indexes; when the first evaluation score is larger than a preset first evaluation threshold, determining a second evaluation dimension based on the first evaluation dimension; based on the secondary evaluation dimension, obtaining a plurality of secondary evaluation indexes; determining a second evaluation score corresponding to the person to be evaluated based on the plurality of second-level evaluation indexes; based on the first evaluation score and the second evaluation score, obtaining a comprehensive evaluation score corresponding to the person to be evaluated; and obtaining a talent guiding effect evaluation result of the high-end industry based on the comprehensive evaluation score.
By adopting the technical scheme, the first-level evaluation dimension is determined based on the evaluation target requirement, so that the tight correlation between an evaluation system and the strategic target and business requirement of high-end industrial enterprises is ensured, and the pertinence and the effectiveness of evaluation are enhanced; determining a plurality of primary evaluation metrics based on the primary evaluation dimensions, thereby materializing the broad evaluation dimensions into quantifiable and operable evaluation criteria; determining a first evaluation score corresponding to the person to be evaluated based on the first-level evaluation indexes, so as to quantitatively evaluate the basic capability and knowledge level of the person to be evaluated; when the first evaluation score is larger than a preset first evaluation threshold, determining a second evaluation dimension based on the first evaluation dimension, so that deeper and finer evaluation is performed on the personnel meeting the basic requirements; a plurality of second-level evaluation indexes are obtained based on the second-level evaluation dimension, so that the evaluation process is further refined, and the talent guiding effect is more comprehensive; the second evaluation scores corresponding to the personnel to be evaluated are determined based on the plurality of second-level evaluation indexes, and the comprehensive evaluation scores corresponding to the personnel to be evaluated are obtained based on the first evaluation scores and the second evaluation scores, so that the calculated comprehensive evaluation scores are more accurate, and the comprehensiveness and accuracy of the obtained high-end industry talent introduction effect evaluation results can be effectively improved.
Optionally, the determining a plurality of first-level evaluation indexes based on the first-level evaluation dimension specifically includes: acquiring a first mapping relation between a first-level evaluation dimension and a key evaluation field; obtaining a first key evaluation field corresponding to the current first-level evaluation dimension based on the first mapping relation; identifying the first key evaluation field to obtain a plurality of first evaluation standards; and formulating a plurality of first-level evaluation indexes based on a plurality of first evaluation standards.
By adopting the technical scheme, the first mapping relation between the first-level evaluation dimension and the key evaluation field is obtained, and the first key evaluation field corresponding to the current first-level evaluation dimension is obtained based on the first mapping relation, so that each first-level evaluation dimension is deeply understood and refined, the evaluation process is more specific and targeted, and the evaluation accuracy is improved. The first key evaluation fields are identified to obtain a plurality of first evaluation standards, so that the abstract evaluation fields are converted into specific evaluation standards, and the evaluation standards are further quantized into specific evaluation indexes by formulating a plurality of first-level evaluation indexes based on the plurality of first evaluation standards, so that the evaluation process is more quantized and objective.
Optionally, the determining, based on the plurality of first-level evaluation indexes, a first evaluation score corresponding to the person to be evaluated specifically includes: acquiring a first-level index value corresponding to each first-level evaluation index of a person to be evaluated; screening a plurality of first-level key index values from the first-level index values; the first-level key index value is a first-level index value larger than a preset first-level index threshold value; determining a first-level maximum key index value from a plurality of first-level key index values; the first-level maximum key index value is larger than or equal to any one of the first-level key index values; and calculating the first evaluation score based on the first-level maximum key index value and a plurality of first-level key index values.
By adopting the technical scheme, a specific data basis is provided for the evaluation process by acquiring the first-level index values corresponding to the first-level evaluation indexes of the personnel to be evaluated, and a plurality of first-level key index values are obtained by screening from the plurality of first-level index values, so that the indexes which are most critical and important for the evaluation are focused, and the evaluation accuracy is improved; because the first-level key index value is a first-level index value larger than a preset first-level index threshold value, only the key fields which show up to or exceeding a preset standard are focused, and the evaluation accuracy is enhanced; the first evaluation score is obtained by determining the first-level maximum key index value from the first-level key index values and calculating the first evaluation score based on the first-level maximum key index value and the first-level key index values, so that the comprehensive performance of individuals in all key fields is combined, and the first evaluation score is more accurate.
Optionally, the calculating to obtain the first evaluation score based on the first-level maximum key index value and the first-level key index values specifically includes: the first evaluation score is calculated by the following formula:
wherein K is 1 For the first evaluation score, n is the number corresponding to the first-level evaluation index, S i For the ith one of the first-level key index values, S max For the first-level maximum key index value, w i And a first-level influence weight corresponding to the i-th first-level key index value is obtained, and alpha is an adjustment coefficient.
Optionally, the obtaining the second-level evaluation dimension based on the first-level evaluation dimension specifically includes: extracting the characteristics of the first-level evaluation dimension to obtain a plurality of first-level evaluation characteristics; carrying out feature splitting on each first-level evaluation feature to obtain a plurality of first-level evaluation sub-features; performing cluster analysis on the plurality of first-level evaluation sub-features to obtain a plurality of preliminary features; verifying a plurality of the preliminary features based on preset priori knowledge to obtain a plurality of advanced features; the secondary evaluation dimension is constructed based on a plurality of the advanced features.
By adopting the technical scheme, the first-level evaluation dimension is subjected to feature extraction to obtain a plurality of first-level evaluation features, so that the first-level evaluation dimension is thinned into more specific features, and the detail degree and the accuracy of the evaluation process are enhanced; the characteristic splitting is carried out on each first-level evaluation characteristic to obtain a plurality of first-level evaluation sub-characteristics, so that the evaluation dimension is further refined, and the evaluation process is more specific and targeted; the method comprises the steps of carrying out cluster analysis on a plurality of primary evaluation sub-features to obtain a plurality of primary features, so that the associated sub-features are combined together to form more concentrated and consistent evaluation dimensions, and verifying the plurality of primary features based on preset priori knowledge to obtain a plurality of advanced features, so that the obtained features are not only theoretically reasonable, but also are closely related to actual working situations, and the practicability and the relevance of an evaluation result are enhanced; by constructing the second-level evaluation dimension based on a plurality of advanced features, the capability of the personnel to be evaluated is comprehensively and deeply reflected on a higher level by the evaluation system, so that the evaluation is more comprehensive and accurate.
Optionally, the determining, based on the plurality of second-level evaluation indexes, a second evaluation score corresponding to the person to be evaluated specifically includes: determining the mutual influence coefficient between the two-level evaluation indexes; acquiring a corresponding secondary index value of the person to be evaluated under each secondary evaluation index; screening a plurality of secondary key index values from the secondary index values; the secondary key index value is a secondary index value larger than a preset secondary index threshold value; determining a second-level maximum key index value from a plurality of second-level key index values; the second-level maximum key index value is larger than or equal to any one of the second-level key index values; and calculating the second evaluation score based on the second-level maximum key index value, a plurality of second-level key index values and a plurality of mutual influence coefficients.
By adopting the technical scheme, a specific data basis is provided for the evaluation process by acquiring the corresponding secondary index values of the personnel to be evaluated under each secondary evaluation index, and a plurality of secondary key index values are obtained by screening from a plurality of secondary index values, so that the indexes which are most critical and important for the evaluation are focused, and the evaluation accuracy is improved; because the secondary key index value is a secondary index value larger than a preset secondary index threshold value, only the key fields which show up to or exceeding a preset standard are focused, and the evaluation accuracy is enhanced; the second maximum key index value is determined from the plurality of second key index values, and the second evaluation score is calculated based on the second maximum key index value, the plurality of second key index values and the plurality of interaction coefficients, so that comprehensive performance of individuals in all key fields is combined, interaction among the second evaluation indexes is fully considered, and the second evaluation score is more accurate.
Optionally, the calculating to obtain the second evaluation score based on the second-level maximum key index value, the plurality of second-level key index values, and the plurality of interaction coefficients specifically includes: the second evaluation score is calculated by the following formula:
wherein K is 2 For the second evaluation score, m is the number corresponding to the second evaluation index, T a For the a-th secondary key index value, T max For the second-level maximum key index value, w a For the second-level influence weight epsilon corresponding to the second-level key index value a ab And beta is a first nonlinear adjustment coefficient, and gamma is a second nonlinear adjustment coefficient.
In a second aspect of the application, a high-end industrial talent promotion effect evaluation system is provided, which comprises an evaluation dimension determination module, an evaluation index determination module and a processing module; the evaluation dimension determining module is used for determining a first-level evaluation dimension based on the evaluation target requirement; the evaluation index determining module is used for determining a plurality of first-level evaluation indexes based on the first-level evaluation dimension; the processing module is used for determining a first evaluation score corresponding to the person to be evaluated based on the plurality of first-level evaluation indexes; the evaluation dimension determining module is further configured to determine a second-level evaluation dimension based on the first-level evaluation dimension when the first evaluation score is greater than a preset first evaluation threshold; the evaluation index determining module is further used for obtaining a plurality of secondary evaluation indexes based on the secondary evaluation dimension; the processing module is further used for determining a second evaluation score corresponding to the person to be evaluated based on the plurality of second-level evaluation indexes; the processing module is further used for obtaining comprehensive evaluation scores corresponding to the personnel to be evaluated based on the first evaluation score and the second evaluation score; the processing module is also used for obtaining a talent guiding effect evaluation result of the high-end industry based on the comprehensive evaluation score.
In a third aspect the present application provides an electronic device comprising a processor, a memory for storing instructions, a user interface and a network interface for communicating to other devices, the processor being arranged to execute the instructions stored in the memory to cause the electronic device to perform a method according to any one of the first aspects of the present application.
In a fourth aspect of the present application there is provided a computer readable storage medium storing a computer program capable of being loaded by a processor and performing a method according to any one of the first aspects of the present application.
In summary, one or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantages:
1. the first-level evaluation dimension is determined based on the evaluation target requirement, so that the tight correlation between an evaluation system and the strategic target and business requirement of high-end industrial enterprises is ensured, and the pertinence and the effectiveness of evaluation are enhanced; determining a plurality of primary evaluation metrics based on the primary evaluation dimensions, thereby materializing the broad evaluation dimensions into quantifiable and operable evaluation criteria; determining a first evaluation score corresponding to the person to be evaluated based on the first-level evaluation indexes, so as to quantitatively evaluate the basic capability and knowledge level of the person to be evaluated; when the first evaluation score is larger than a preset first evaluation threshold, determining a second evaluation dimension based on the first evaluation dimension, so that deeper and finer evaluation is performed on the personnel meeting the basic requirements; a plurality of second-level evaluation indexes are obtained based on the second-level evaluation dimension, so that the evaluation process is further refined, and the talent guiding effect is more comprehensive; the second evaluation scores corresponding to the personnel to be evaluated are determined based on the plurality of second-level evaluation indexes, and the comprehensive evaluation scores corresponding to the personnel to be evaluated are obtained based on the first evaluation scores and the second evaluation scores, so that the calculated comprehensive evaluation scores are more accurate, and the comprehensiveness and accuracy of the obtained high-end industry talent introduction effect evaluation results can be effectively improved.
2. The first mapping relation between the first-level evaluation dimension and the key evaluation field is obtained, and the first key evaluation field corresponding to the current first-level evaluation dimension is obtained based on the first mapping relation, so that each first-level evaluation dimension is deeply understood and refined, the evaluation process is more specific and targeted, and the evaluation accuracy is improved. The first key evaluation fields are identified to obtain a plurality of first evaluation standards, so that the abstract evaluation fields are converted into specific evaluation standards, and the evaluation standards are further quantized into specific evaluation indexes by formulating a plurality of first-level evaluation indexes based on the plurality of first evaluation standards, so that the evaluation process is more quantized and objective.
3. The first-level evaluation dimension is subjected to feature extraction to obtain a plurality of first-level evaluation features, so that the first-level evaluation dimension is thinned into more specific features, and the detail degree and the accuracy of the evaluation process are enhanced; the characteristic splitting is carried out on each first-level evaluation characteristic to obtain a plurality of first-level evaluation sub-characteristics, so that the evaluation dimension is further refined, and the evaluation process is more specific and targeted; the method comprises the steps of carrying out cluster analysis on a plurality of primary evaluation sub-features to obtain a plurality of primary features, so that the associated sub-features are combined together to form more concentrated and consistent evaluation dimensions, and verifying the plurality of primary features based on preset priori knowledge to obtain a plurality of advanced features, so that the obtained features are not only theoretically reasonable, but also are closely related to actual working situations, and the practicability and the relevance of an evaluation result are enhanced; by constructing the second-level evaluation dimension based on a plurality of advanced features, the capability of the personnel to be evaluated is comprehensively and deeply reflected on a higher level by the evaluation system, so that the evaluation is more comprehensive and accurate.
Drawings
FIG. 1 is a schematic flow chart of a method for evaluating talent guiding effect of high-end industry according to an embodiment of the present application;
FIG. 2 is a second flow chart of a method for evaluating talent guiding effect of high-end industries according to the embodiment of the present application;
FIG. 3 is a third flow chart of a method for evaluating talent promotion effect of high-end industries according to the embodiments of the present application;
fig. 4 is a schematic structural diagram of a talent guiding effect evaluation system for high-end industries according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Reference numerals illustrate: 1. an evaluation dimension determination module; 2. an evaluation index determining module; 3. a processing module; 4. an acquisition module; 500. an electronic device; 501. a processor; 502. a communication bus; 503. a user interface; 504. a network interface; 505. a memory.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in the present specification, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is obvious that the described embodiments are only some embodiments of the present application, but not all embodiments.
In the description of embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described herein as "such as" or "for example" should not be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "or" for example "is intended to present related concepts in a concrete fashion.
In the description of the embodiments of the present application, the term "plurality" means two or more. For example, a plurality of systems means two or more systems, and a plurality of screen terminals means two or more screen terminals. Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating an indicated technical feature. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless expressly specified otherwise.
The application provides a high-end industry talent guiding effect evaluation method, and referring to fig. 1, one of the flow diagrams of the high-end industry talent guiding effect evaluation method provided in the embodiment of the application is shown. The method comprises the steps S1-S8, wherein the steps are as follows:
step S1: and determining a first-level evaluation dimension based on the evaluation target requirement.
Specifically, in the technical scheme, a first-level evaluation dimension, namely, an origin of talent introduction effect evaluation of the whole high-end industry is determined.
Before determining the first-order evaluation dimension, the target demand needs to be explicitly evaluated first. Namely, determining strategic targets, business requirements and talent development planning of high-end industrial enterprises. And then, based on the evaluation target requirement, a specific first-level evaluation dimension is formulated. Identifying which skills and capabilities are most critical to the high-end industry enterprise. For example, for a technology driven enterprise, "technical innovation capability," "project management," "team collaboration," etc. are required as primary evaluation dimensions. Finally, the implementation effect of the first-level evaluation dimension is evaluated to judge whether the first-level evaluation dimension can effectively help the high-end industrial enterprises to identify and cultivate talents, keep the same with the business targets and market development trends of the high-end industrial enterprises, and provide support for future talent strategies. Therefore, the effectiveness and adaptability of the first-level evaluation dimension are ensured, and the effect and efficiency of the whole evaluation system are improved.
Step S2: a plurality of primary evaluation metrics are determined based on the primary evaluation dimensions.
Specifically, in the technical scheme, a plurality of first-level evaluation indexes are determined based on first-level evaluation dimensions, namely, abstract evaluation dimensions are embodied, so that the abstract evaluation dimensions become quantifiable and evaluable standards.
In one possible implementation, step S2 specifically includes the following steps:
and acquiring a first mapping relation between the first-level evaluation dimension and the key evaluation field.
Specifically, in the present technical solution, the primary task of step S2 is to understand and map the relationship between the primary evaluation dimension and the key evaluation field. This process requires in-depth analysis of the specific work content, employee duties and desired outcomes of the high-end industrial enterprise. For example, if the primary evaluation dimension is "technical expertise," relevant key evaluation areas may include "software development capability", "technical innovation" and "technical application efficiency". By establishing a mapping relation between the first-level evaluation dimension and the key evaluation field, the close correlation between the evaluation index and the actual demand of the high-end industrial enterprise can be ensured.
And obtaining a first key evaluation field corresponding to the current first-level evaluation dimension based on the first mapping relation.
Specifically, in the technical scheme, after the first mapping relation between the first-level evaluation dimension and the key evaluation domain is obtained, the key evaluation domain corresponding to the current first-level evaluation dimension is determined based on the first mapping relation. This step requires specific identification of which areas of evaluation are critical for measuring the corresponding dimensions. In the above example, for the dimension of "technical expertise", it is further required to be subdivided into specific key evaluation fields, such as "front-end development", "data analysis", and the like.
And identifying the first key evaluation field to obtain a plurality of first evaluation standards.
Specifically, in the technical scheme, the first key evaluation field is deeply identified and analyzed, and a specific evaluation standard is extracted. It relates to the refinement of various capabilities and skills in the evaluation field, such as in the field of "front-end development", the first evaluation criteria may include "programming language proficiency", "interface design ability", and the like. Such refinement helps to make the assessment more specific and targeted, improving the effectiveness and efficiency of the assessment.
A plurality of first-level evaluation indexes are formulated based on the plurality of first evaluation criteria.
Specifically, in the technical scheme, a specific first-level evaluation index is finally formulated based on a plurality of first evaluation criteria. At this stage, each evaluation criterion will be converted into a specific, quantifiable evaluation index.
Step S3: and determining a first evaluation score corresponding to the person to be evaluated based on the plurality of first-level evaluation indexes.
Specifically, in the technical scheme, the first evaluation score of the personnel to be evaluated is determined based on the first-level evaluation index, namely, the first-level evaluation index is converted into a specific numerical score, so that a quantitative basis is provided for subsequent evaluation decisions.
In a possible implementation manner, reference is made to fig. 2, which shows a second flow chart of a high-end industrial talent guiding effect evaluation method provided in the embodiment of the present application. The step S3 specifically comprises the steps S31-S34:
step S31: and obtaining a first-level index value corresponding to each first-level evaluation index of the personnel to be evaluated.
Specifically, in the technical scheme, the manner of acquiring the first-level index value corresponding to each first-level evaluation index of the person to be evaluated first needs to determine which data or information sources can effectively reflect the performance of the person to be evaluated under each first-level evaluation index, which includes, but is not limited to, personal performance records, colleagues and superior feedback, customer evaluation, project reports and the like. A standardized set of methods is then set up to collect and record such data, for example, by designing questionnaires, conducting face-to-face interviews, collecting and analyzing performance reports, and the like. After the data is collected, it is converted into a first level index value, for example, the feedback score is converted into a normalized score, and the performance result is compared with a preset reference.
Step S32: screening a plurality of first-level key index values from the plurality of first-level index values; the first-level key index value is a first-level index value larger than a preset first-level index threshold value.
Specifically, in the present technical solution, a preset first-level index threshold needs to be set first. The preset primary index threshold is determined based on performance criteria and evaluation targets of the high-end industrial enterprise, which represents the lowest criteria that are considered to be excellent or critical performance among the respective primary evaluation indexes. For example, in the evaluation dimension of a technical expertise, a specific skill test score or project success rate may be set as a threshold. And comparing the first-level index values with a preset first-level index threshold value to judge which index values meet or exceed the standard.
Step S33: determining a first-level maximum key index value from a plurality of first-level key index values; the first-level maximum key index value is larger than or equal to any one first-level key index value.
Specifically, in the technical scheme, step S33 focuses on determining a first-level maximum key index value from a plurality of first-level key index values, so as to identify the most prominent field of the personnel to be evaluated in all the first-level key indexes, and highlight the strongest capability or achievement of the personnel to be evaluated, thereby providing an important basis for comprehensive evaluation.
Step S34: and calculating to obtain a first evaluation score based on the first-level maximum key index value and the plurality of first-level key index values.
In one possible implementation, step S34 specifically includes the following steps:
the first evaluation score was calculated by the following formula:
wherein K is 1 For the first evaluation score, n is the number corresponding to the first-level evaluation index, S i Is the i-th level key index value S max Is the first-level maximum key index value, w i And the first-level influence weight corresponding to the ith first-level key index value is obtained, and alpha is an adjustment coefficient.
Specifically, in the present technical solution, each oneScore S of level key index i And corresponding weight w i Is the basis of the formula that reflects the performance of each index and its relative importance in the overall evaluation. Weight w i Is set based on the influence of the contribution degree of the index to the high-end industry enterprise.
Introducing an adjustment coefficient alpha and a first-order maximum key index value S max The purpose of (2) is to emphasize the salient performance of an individual in a critical area. Which embodies a bonus for outstanding performance in calculating the first rating score. When a certain level of key index value S i Near or reaching S in all indexes max The score of the index will be additionally improved.
Step S4: and determining a second evaluation dimension based on the first evaluation dimension when the first evaluation score is greater than a preset first evaluation threshold.
Specifically, in the present technical solution, the implementation of step S4 aims at extracting a more specific evaluation domain from the broad classification of the primary evaluation dimension, i.e. determining the secondary evaluation dimension, so as to help identify the specific performance and potential ability of the person to be evaluated in the more subdivided domain.
In one possible implementation, step S4 specifically includes the following steps:
and extracting the characteristics of the first-level evaluation dimension to obtain a plurality of first-level evaluation characteristics.
Specifically, in the present technical solution, first, the primary evaluation dimension is evaluated, for example, if the primary evaluation dimension is "leader capability", then deep exploration and analysis are required for different aspects of the leader capability, and then, a plurality of primary evaluation features are identified from the extensive primary evaluation dimension through data mining and analysis technology.
And carrying out feature splitting on each first-level evaluation feature to obtain a plurality of first-level evaluation sub-features.
Specifically, in the technical scheme, each first-level evaluation feature is refined and split, so that more specific first-level evaluation sub-features are obtained, and the wide evaluation dimension is converted into more specific and subdivided evaluation points. For example, "leadership" may be deepened into a first level of evaluation features such as "decision making", "team motivation", and the like. In this step, these first-level evaluation features are further refined into more specific first-level evaluation sub-features. For example, "decision making" can be further broken down into "risk assessment", "policy selection", etc. primary evaluation sub-features.
And carrying out cluster analysis on the plurality of first-level evaluation sub-features to obtain a plurality of preliminary features.
Specifically, in the present solution, a cluster analysis method is used to process a plurality of first-level evaluation sub-features. Cluster analysis is a statistical technique for grouping data points so that members within a group have more similarity than members between groups. In this process, similar or related, first-level evaluation sub-features will be grouped together to form a new, more focused set of evaluation dimensions, resulting in a plurality of preliminary features.
And verifying the plurality of preliminary features based on preset priori knowledge to obtain a plurality of advanced features.
Specifically, in the present solution, the prior knowledge may come from industry standards, opinion of field experts, past research results, or historical data and experience inside the company. The plurality of preliminary features are verified based on preset priori knowledge, so that an evaluation system can be ensured to be based on theory and data analysis, and the actual working situation and requirements of high-end industrial enterprises are closely combined.
A second-level evaluation dimension is constructed based on the plurality of high-level features.
Specifically, in the technical scheme, the preliminary features verified in the previous steps are integrated and recombined to form a more comprehensive secondary evaluation dimension. The implementation method comprises the steps of carrying out deep analysis on the relation and interaction of the primary characteristics so as to ensure that the newly constructed secondary evaluation dimension has logical consistency and can comprehensively reflect the abilities of staff in different fields. For example, if multiple features related to team cooperation, communication skills, and leadership are found in the preliminary features, these related features may be integrated into a broader secondary evaluation dimension, such as "team leadership and collaborative ability.
Step S5: and obtaining a plurality of secondary evaluation indexes based on the secondary evaluation dimension.
Specifically, in the technical scheme, a specific method for obtaining a plurality of secondary evaluation indexes based on the secondary evaluation dimension is similar to a method for obtaining a plurality of primary evaluation indexes based on the primary evaluation dimension, so that redundant description is omitted herein.
Step S6: and determining a second evaluation score corresponding to the person to be evaluated based on the plurality of second-level evaluation indexes.
Specifically, in the technical scheme, the second evaluation score of the person to be evaluated is determined based on the second evaluation index, namely, the second evaluation index is converted into a specific numerical score, so that a quantitative basis is provided for subsequent evaluation decisions.
In one possible implementation manner, referring to fig. 3, a third flow chart of a high-end industrial talent guiding effect evaluation method provided in the embodiment of the present application is shown. The step S6 specifically comprises the steps S61-S65:
step S61: and determining the mutual influence coefficient between the two secondary evaluation indexes.
Specifically, in the present technical solution, in actual work, the performance of one ability area of an individual often affects other areas. For example, an excellent leader may promote team cooperation effects and vice versa. By identifying and quantifying these interactions, the assessment system can more truly reflect the overall performance and potential of the employee. Step S61 therefore first requires an in-depth analysis of the determined secondary evaluation indicators to identify possible correlations or effects between these indicators. Such analysis involves data mining, statistical analysis, or expert consultation to determine the strength and direction of the relationship between the different indices. For example, if one of the two secondary ratings is "team collaborative capability" and the other is "leadership," then there may be a positive or negative impact relationship between the two ratings.
Step S62: and obtaining the corresponding secondary index value of the person to be evaluated under each secondary evaluation index.
Specifically, in the technical scheme, the specific method for acquiring the second-level index value corresponding to each second-level evaluation index of the person to be evaluated is similar to the method for acquiring the first-level index value corresponding to each first-level evaluation index of the person to be evaluated, so that redundant description is omitted herein.
Step S63: screening a plurality of secondary key index values from the plurality of secondary index values; the secondary key index value is a secondary index value larger than a preset secondary index threshold value.
Specifically, in the present technical solution, a specific method for screening a plurality of secondary key index values from a plurality of secondary index values is similar to a specific method for screening a plurality of primary key index values from a plurality of primary index values, so that redundant description is omitted herein.
Step S64: determining a second-level maximum key index value from a plurality of second-level key index values; the second-level maximum key index value is larger than or equal to any one second-level key index value.
Specifically, in the present technical solution, a specific method for determining a second-level maximum key index value from a plurality of second-level key index values is similar to a specific method for determining a first-level maximum key index value from a plurality of first-level key index values, so that redundant description is omitted herein.
Step S65: and calculating a second evaluation score based on the second-level maximum key index value, the plurality of second-level key index values and the plurality of mutual influence coefficients.
In one possible implementation, step S65 specifically includes the following steps:
the second evaluation score was calculated by the following formula:
wherein K is 2 For the second evaluation score, m is the number corresponding to the second evaluation index, T a Is the a second level key index value, T max Is the maximum of the second levelKey index value, w a For the second-level influence weight epsilon corresponding to the a-th second-level key index value ab Beta is a first nonlinear adjustment coefficient, and gamma is a second nonlinear adjustment coefficient, for the mutual influence coefficients.
In particular, in the present solution, in some cases, the overall performance of the individual is more important than the sum of the individual metrics. For example, if one person performs equally across all the evaluation metrics, it may be more reliable than another person who performs prominently on some metrics but performs poorly on other metrics. Thus, the nonlinear adjustment is introduced by the first nonlinear adjustment coefficient and the second nonlinear adjustment coefficient, the first nonlinear adjustment coefficient adjusting the magnitude of the overall influence of the nonlinear effect. The second nonlinear adjustment factor determines the nonlinear shape, e.g., makes the score curve steeper or flatter. So that the formula is not focused on only a few indices. When a certain person to be evaluated performs well on all indexes, the overall evaluation score of the person to be evaluated can be additionally improved due to the nonlinear increase, so that the influence of abnormal high score or low score of a single index on the overall score can be balanced, and the comprehensive capacity and performance of the person can be more comprehensively reflected.
Step S7: and obtaining the comprehensive evaluation score corresponding to the person to be evaluated based on the first evaluation score and the second evaluation score.
Specifically, in the present solution, it is first necessary to determine an appropriate method to integrate the two evaluation scores. Different weights can be set for each evaluation score to reflect the importance of high-end industry enterprises to different capacity levels. For example, if a high-end industrial enterprise attaches more importance to basic skills, the first evaluation score K1 may be given a higher weight; the second evaluation score K2 may be weighted more if the high-end industrial enterprise is more concerned with expertise or leadership.
Step S8: and obtaining a talent introduction effect evaluation result of the high-end industry based on the comprehensive evaluation score.
Specifically, in the technical scheme, the step integrates the results of all previous evaluation steps to form the comprehensive evaluation of the comprehensive capacity and potential of the personnel to be evaluated. These composite evaluation scores need to be compared with a pre-set standard or threshold. A performance level within a particular score range, such as "excellent," "good," "general," etc., is determined or the score is associated with a particular talent development path or job requirement. For example, a higher overall rating score may indicate that an individual is suitable to take on a higher level of position or assume greater responsibility.
Referring to fig. 4, a schematic structural diagram of a high-end industrial talent guiding effect evaluation system provided in an embodiment of the present application is shown, where the system includes a dimension determining module 1, an evaluation index determining module 2, and a processing module 3; the evaluation dimension determining module 1 is used for determining a first-level evaluation dimension based on the evaluation target requirement; an evaluation index determination module 2, configured to determine a plurality of primary evaluation indexes based on the primary evaluation dimensions; the processing module 3 is used for determining a first evaluation score corresponding to the person to be evaluated based on the plurality of first-level evaluation indexes; the evaluation dimension determining module 1 is further configured to determine a second-level evaluation dimension based on the first-level evaluation dimension when the first evaluation score is greater than a preset first evaluation threshold; the evaluation index determining module 2 is further used for obtaining a plurality of secondary evaluation indexes based on the secondary evaluation dimension; the processing module 3 is further used for determining a second evaluation score corresponding to the person to be evaluated based on the plurality of second-level evaluation indexes; the processing module 3 is further used for obtaining comprehensive evaluation scores corresponding to the personnel to be evaluated based on the first evaluation score and the second evaluation score; and the processing module 3 is also used for obtaining the talent guiding effect evaluation result of the high-end industry based on the comprehensive evaluation score.
In a possible implementation manner, the system further comprises an obtaining module 4, where the obtaining module 4 is configured to obtain a first mapping relationship between the first-level evaluation dimension and the key evaluation field; the evaluation index determining module 2 is further configured to obtain a related first key evaluation field corresponding to the current first-level evaluation dimension based on the first mapping relationship; the evaluation index determining module 2 is further configured to identify a first key evaluation domain, so as to obtain a plurality of first evaluation criteria; the evaluation index determination module 2 is further configured to formulate a plurality of first-level evaluation indexes based on the plurality of first evaluation criteria.
In a possible implementation manner, the obtaining module 4 is further configured to obtain a first-level index value corresponding to each first-level evaluation index of the person to be evaluated; the processing module 3 is further used for screening a plurality of first-level key index values from the plurality of first-level index values; the first-level key index value is a first-level index value larger than a preset first-level index threshold value; the processing module 3 is further used for determining a first-level maximum key index value from a plurality of first-level key index values; the first-level maximum key index value is larger than or equal to any one first-level key index value; the processing module 3 is further configured to calculate a first evaluation score based on the first-level maximum key index value and the plurality of first-level key index values.
In a possible embodiment, the processing module 3 is further configured to calculate the first evaluation score by the following formula:
wherein K is 1 For the first evaluation score, n is the number corresponding to the first-level evaluation index, S i Is the i-th level key index value S max Is the first-level maximum key index value, w i And the first-level influence weight corresponding to the ith first-level key index value is obtained, and alpha is an adjustment coefficient.
In a possible implementation manner, the dimension determining module 1 is further configured to perform feature extraction on the first-level evaluation dimension to obtain a plurality of first-level evaluation features; the dimension determining module 1 is further used for carrying out feature splitting on each first-level evaluation feature to obtain a plurality of first-level evaluation sub-features; the dimension determining module 1 is further used for carrying out cluster analysis on the plurality of first-level evaluation sub-features to obtain a plurality of preliminary features; the dimension determining module 1 is further configured to verify the plurality of preliminary features based on preset priori knowledge to obtain a plurality of advanced features; the dimension determining module 1 is further configured to construct a second-level evaluation dimension based on the plurality of advanced features.
In a possible embodiment, the processing module 3 is further configured to determine a coefficient of interaction between the respective second-level evaluation indicators; the acquisition module 4 is further used for acquiring a second-level index value corresponding to each second-level evaluation index of the person to be evaluated; the processing module 3 is further used for screening a plurality of secondary key index values from the plurality of secondary index values; the secondary key index value is a secondary index value larger than a preset secondary index threshold value; the processing module 3 is further used for determining a second-level maximum key index value from a plurality of second-level key index values; the second-level maximum key index value is larger than or equal to any one second-level key index value; the processing module 3 is further configured to calculate a second evaluation score based on the second-level maximum key index value, the plurality of second-level key index values, and the plurality of interaction coefficients.
In a possible embodiment, the processing module 3 is further configured to calculate the second evaluation score by the following formula:
wherein K is 2 For the second evaluation score, m is the number corresponding to the second evaluation index, T a Is the a second level key index value, T max Is the second-level maximum key index value, w a For the second-level influence weight epsilon corresponding to the a-th second-level key index value ab Beta is a first nonlinear adjustment coefficient, and gamma is a second nonlinear adjustment coefficient, for the mutual influence coefficients.
It should be noted that: in the device provided in the above embodiment, when implementing the functions thereof, only the division of the above functional modules is used as an example, in practical application, the above functional allocation may be implemented by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules, so as to implement all or part of the functions described above. In addition, the embodiments of the apparatus and the method provided in the foregoing embodiments belong to the same concept, and specific implementation processes of the embodiments of the method are detailed in the method embodiments, which are not repeated herein.
The application also discloses electronic equipment. Referring to fig. 5, fig. 5 is a schematic structural diagram of an electronic device according to the disclosure in an embodiment of the present application. The electronic device 500 may include: at least one processor 501, at least one network interface 504, a user interface 503, a memory 505, at least one communication bus 502.
Wherein a communication bus 502 is used to enable connected communications between these components.
The user interface 503 may include a Display screen (Display) and a Camera (Camera), and the optional user interface 503 may further include a standard wired interface and a standard wireless interface.
The network interface 504 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), among others.
Wherein the processor 501 may include one or more processing cores. The processor 501 connects various parts throughout the server using various interfaces and lines, performs various functions of the server and processes data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory 505, and invoking data stored in the memory 505. Alternatively, the processor 501 may be implemented in hardware in at least one of digital signal processing (DigitalSignalProcessing, DSP), field programmable gate array (Field-ProgrammableGateArray, FPGA), and programmable logic array (ProgrammableLogicArray, PLA). The processor 501 may integrate one or a combination of several of a central processor (CentralProcessingUnit, CPU), an image processor (GraphicsProcessingUnit, GPU), a modem, etc. The CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for rendering and drawing the content required to be displayed by the display screen; the modem is used to handle wireless communications. It will be appreciated that the modem may not be integrated into the processor 501 and may be implemented by a single chip.
The memory 505 may include a random access memory (RandomAccessMemory, RAM) or a Read-only memory (rom). Optionally, the memory 505 comprises a non-transitory computer readable medium (non-transitoroompter-readabblestonemam). Memory 505 may be used to store instructions, programs, code sets, or instruction sets. The memory 505 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the above-described various method embodiments, etc.; the storage data area may store data or the like involved in the above respective method embodiments. The memory 505 may also optionally be at least one storage device located remotely from the processor 501. Referring to fig. 5, an operating system, a network communication module, a user interface module, and an application program may be included in the memory 505, which is a computer readable storage medium.
In the electronic device 500 shown in fig. 5, the user interface 503 is mainly used for providing an input interface for a user, and acquiring data input by the user; and the processor 501 may be configured to invoke the memory 505 to store an application program that, when executed by the one or more processors 501, causes the electronic device 500 to perform the method as in one or more of the embodiments described above. It should be noted that, for simplicity of description, the foregoing method embodiments are all expressed as a series of action combinations, but it should be understood by those skilled in the art that the present application is not limited by the order of actions described, as some steps may be performed in other order or simultaneously in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required in the present application.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to the related descriptions of other embodiments.
In the several embodiments provided herein, it should be understood that the disclosed apparatus may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, such as a division of units, merely a division of logic functions, and there may be additional divisions in actual implementation, such as multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some service interface, device or unit indirect coupling or communication connection, electrical or otherwise.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable memory. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a memory, including several instructions for causing a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the methods of the embodiments of the present application. And the aforementioned memory includes: various media capable of storing program codes, such as a U disk, a mobile hard disk, a magnetic disk or an optical disk.
The above are merely exemplary embodiments of the present disclosure and are not intended to limit the scope of the present disclosure. That is, equivalent changes and modifications are contemplated by the teachings of this disclosure, which fall within the scope of the present disclosure. Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure.
This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a scope and spirit of the disclosure being indicated by the claims.

Claims (10)

1. The method for evaluating the talent guiding effect of the high-end industry is characterized by comprising the following steps of:
determining a first-level evaluation dimension based on the evaluation target requirement;
determining a plurality of primary evaluation indexes based on the primary evaluation dimension;
determining a first evaluation score corresponding to the person to be evaluated based on the plurality of first-level evaluation indexes;
when the first evaluation score is larger than a preset first evaluation threshold, determining a second evaluation dimension based on the first evaluation dimension; based on the secondary evaluation dimension, obtaining a plurality of secondary evaluation indexes;
determining a second evaluation score corresponding to the person to be evaluated based on the plurality of second-level evaluation indexes;
based on the first evaluation score and the second evaluation score, obtaining a comprehensive evaluation score corresponding to the person to be evaluated;
And obtaining a talent guiding effect evaluation result of the high-end industry based on the comprehensive evaluation score.
2. The method according to claim 1, wherein determining a plurality of primary evaluation metrics based on the primary evaluation dimension, in particular comprises:
acquiring a first mapping relation between a first-level evaluation dimension and a key evaluation field;
obtaining a first key evaluation field corresponding to the current first-level evaluation dimension based on the first mapping relation;
identifying the first key evaluation field to obtain a plurality of first evaluation standards;
and formulating a plurality of first-level evaluation indexes based on a plurality of first evaluation standards.
3. The method according to claim 1, wherein the determining a first evaluation score corresponding to the person to be evaluated based on the plurality of the first-level evaluation indexes specifically includes:
acquiring a first-level index value corresponding to each first-level evaluation index of a person to be evaluated;
screening a plurality of first-level key index values from the first-level index values; the first-level key index value is a first-level index value larger than a preset first-level index threshold value;
determining a first-level maximum key index value from a plurality of first-level key index values; the first-level maximum key index value is larger than or equal to any one of the first-level key index values;
And calculating the first evaluation score based on the first-level maximum key index value and a plurality of first-level key index values.
4. The method according to claim 3, wherein the calculating the first evaluation score based on the first-level maximum key index value and the plurality of first-level key index values specifically includes:
the first evaluation score is calculated by the following formula:
wherein K is 1 For the first evaluation score, n is the number corresponding to the first-level evaluation index, S i For the ith one of the first-level key index values, S max For the first-level maximum key index value, w i And a first-level influence weight corresponding to the i-th first-level key index value is obtained, and alpha is an adjustment coefficient.
5. The method according to claim 1, wherein the obtaining a second-level evaluation dimension based on the first-level evaluation dimension specifically comprises:
extracting the characteristics of the first-level evaluation dimension to obtain a plurality of first-level evaluation characteristics;
carrying out feature splitting on each first-level evaluation feature to obtain a plurality of first-level evaluation sub-features;
performing cluster analysis on the plurality of first-level evaluation sub-features to obtain a plurality of preliminary features;
Verifying a plurality of the preliminary features based on preset priori knowledge to obtain a plurality of advanced features;
the secondary evaluation dimension is constructed based on a plurality of the advanced features.
6. The method according to claim 1, wherein the determining the second evaluation score corresponding to the person to be evaluated based on the plurality of the second-level evaluation indexes specifically includes:
determining the mutual influence coefficient between the two-level evaluation indexes;
acquiring a corresponding secondary index value of the person to be evaluated under each secondary evaluation index;
screening a plurality of secondary key index values from the secondary index values; the secondary key index value is a secondary index value larger than a preset secondary index threshold value;
determining a second-level maximum key index value from a plurality of second-level key index values; the second-level maximum key index value is larger than or equal to any one of the second-level key index values;
and calculating the second evaluation score based on the second-level maximum key index value, a plurality of second-level key index values and a plurality of mutual influence coefficients.
7. The method according to claim 6, wherein the calculating the second evaluation score based on the second-level maximum key index value, the plurality of second-level key index values, and the plurality of interaction coefficients specifically includes:
The second evaluation score is calculated by the following formula:
wherein K is 2 For the second evaluation score, m is the number corresponding to the second evaluation index, T a For the a-th secondary key index value, T max For the second-level maximum key index value, w a For the second-level influence weight epsilon corresponding to the second-level key index value a ab And beta is a first nonlinear adjustment coefficient, and gamma is a second nonlinear adjustment coefficient.
8. The system is characterized by comprising an evaluation dimension determining module, an evaluation index determining module and a processing module;
the evaluation dimension determining module is used for determining a first-level evaluation dimension based on the evaluation target requirement;
the evaluation index determining module is used for determining a plurality of first-level evaluation indexes based on the first-level evaluation dimension;
the processing module is used for determining a first evaluation score corresponding to the person to be evaluated based on the plurality of first-level evaluation indexes;
the evaluation dimension determining module is further configured to determine a second-level evaluation dimension based on the first-level evaluation dimension when the first evaluation score is greater than a preset first evaluation threshold;
The evaluation index determining module is further used for obtaining a plurality of secondary evaluation indexes based on the secondary evaluation dimension;
the processing module is further used for determining a second evaluation score corresponding to the person to be evaluated based on the plurality of second-level evaluation indexes;
the processing module is further used for obtaining comprehensive evaluation scores corresponding to the personnel to be evaluated based on the first evaluation score and the second evaluation score;
the processing module is also used for obtaining a talent guiding effect evaluation result of the high-end industry based on the comprehensive evaluation score.
9. An electronic device comprising a processor (501), a memory (505), a user interface (503) and a network interface (504), the memory (505) for storing instructions, the user interface (503) and the network interface (504) for communicating to other devices, the processor (501) for executing the instructions stored in the memory (505) to cause the electronic device (500) to perform the method according to any of claims 1-7.
10. A computer readable storage medium storing instructions which, when executed, perform the method of any one of claims 1-7.
CN202410248889.9A 2024-03-05 2024-03-05 High-end industry talent introduction effect evaluation method and system Pending CN117893098A (en)

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