CN113962664A - Method, device, equipment and medium for human resource evaluation - Google Patents

Method, device, equipment and medium for human resource evaluation Download PDF

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CN113962664A
CN113962664A CN202111279738.2A CN202111279738A CN113962664A CN 113962664 A CN113962664 A CN 113962664A CN 202111279738 A CN202111279738 A CN 202111279738A CN 113962664 A CN113962664 A CN 113962664A
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霍嘉
秦宇琪
刘捷
任长清
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Industrial and Commercial Bank of China Ltd ICBC
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Abstract

The present disclosure provides a method for human resource evaluation, which can be applied to the technical field of artificial intelligence. The method comprises the following steps: setting at least one evaluation rule, wherein each evaluation rule is used for expressing the mapping relation between the job performance data of one post and one human resource evaluation index; constructing a decision tree based on each evaluation rule; training the decision tree by taking the job performance data of the posts related to the evaluation rules in the historical data as sample data and taking the human resource evaluation indexes related to the evaluation rules in the historical data as labels of the sample data to obtain a human resource evaluation model; and evaluating the performance of the staff on the corresponding post on the corresponding human resource evaluation index by using the human resource evaluation model. The present disclosure also provides an apparatus, a device, a storage medium, and a program product for human resources evaluation.

Description

Method, device, equipment and medium for human resource evaluation
Technical Field
The present disclosure relates to the field of artificial intelligence, and more particularly, to a method, apparatus, device, medium, and program product for human resource assessment.
Background
At present, the evaluation of human resources of organizations such as enterprises mainly depends on manual evaluation and static quantitative statistics after the working period is finished, and the evaluation efficiency of human resources is low and the timeliness is not strong due to the mode. For example, as the current work forms are diversified, the work of some high-tech personnel is often no longer fixed in office, the work may be circulated among multiple departments (for example, a business architect), the remote office, and the like, and in this case, the evaluation result is inevitably misqualified because the worker and the manager for evaluating human resources may not work in the same office for a long time. For another example, after a fixed work cycle is over, the performance assessment of short term borrowers, outsourcing personnel, or personnel who are out of work in the middle of the work cycle is not timely enough, nor accurate enough. Therefore, how to objectively and timely evaluate the performance of workers at various positions is very important for the management of human resources.
Disclosure of Invention
In view of the foregoing, embodiments of the present disclosure provide a method, apparatus, device, medium, and program product for human resource assessment that improves timeliness, effectiveness, and accuracy of human resource assessment.
In a first aspect of the disclosed embodiments, a human resource evaluation rule is provided for representing a mapping relationship between position performance data and a human resource evaluation index; constructing a decision tree based on each evaluation rule; training the decision tree by taking the job performance data of the posts related to the evaluation rules in the historical data as sample data and taking the human resource evaluation indexes related to the evaluation rules in the historical data as labels of the sample data; and evaluating the performance of the staff on the corresponding post on the corresponding human resource evaluation index by using the human resource evaluation model.
According to an embodiment of the present disclosure, the setting at least one evaluation rule includes: when the N evaluation rules are respectively used for representing the mapping relation between the position-performing behavior data and N human resource evaluation indexes, the position-performing behavior data in the N evaluation rules are set to be the same but with different algorithms, wherein N is an integer greater than or equal to 2.
According to an embodiment of the present disclosure, the constructing a decision tree based on each of the evaluation rules includes: and constructing N decision trees based on the N evaluation rules, wherein the N decision trees share input data.
According to the embodiment of the disclosure, the N human resource evaluation indexes comprise a work contribution index and a compensation cost index.
According to an embodiment of the present disclosure, the evaluating, by using the human resource evaluation model, the performance of the staff on the corresponding post on the corresponding human resource evaluation index includes: acquiring first job performance data of a first worker based on the job performance data of the positions involved in the evaluation rules corresponding to the human resource evaluation model; and processing the first job performance data by using the human resource evaluation model, and outputting a first index value of the first worker on the corresponding human resource evaluation index.
According to an embodiment of the present disclosure, the evaluating, by using the human resource evaluation model, the performance of the staff on the corresponding post on the corresponding human resource evaluation index further includes: and outputting the final value of the corresponding human resource evaluation index of the first worker based on the adjustment or confirmation of the first index value by the manager.
According to an embodiment of the present disclosure, the training the decision tree further comprises: and merging the first job-related behavior data into the sample data, and taking the final value as a label of the first job-related behavior data to participate in the training of the decision tree.
In a second aspect of the disclosed embodiments, an apparatus for human resources evaluation is provided. The device comprises an evaluation rule input and adjustment module and a data analysis and intelligent algorithm module. The evaluation rule recording and adjusting module is used for setting at least one evaluation rule, and each evaluation rule is used for expressing the mapping relation between the job performance data of one post and one human resource evaluation index. The data analysis and intelligent algorithm module comprises a decision tree construction submodule, a training submodule and an evaluation submodule. The decision tree construction submodule is used for constructing a decision tree based on each evaluation rule. And the training submodule is used for training the decision tree by taking the job performance data of the posts related to the evaluation rules in the historical data as sample data and taking the human resource evaluation indexes related to the evaluation rules in the historical data as labels of the sample data. And the evaluation submodule is used for evaluating the performance of the corresponding staff on the post on the corresponding human resource evaluation index by using the human resource evaluation model.
According to an embodiment of the present disclosure, the apparatus further comprises a data acquisition and capture module. The data acquisition and capture module is used for acquiring first job performance data of a first worker based on the job performance data of the positions related in the evaluation rules corresponding to the human resource evaluation model. The evaluation submodule is used for processing the first job-performing behavior data by using the human resource evaluation model and outputting a first index value of the first worker on the corresponding human resource evaluation index.
According to an embodiment of the present disclosure, the apparatus further comprises a query and management module. And the query management module is used for outputting the final value of the human resource evaluation index based on the adjustment or confirmation of the first index value by a manager.
According to an embodiment of the present disclosure, the training sub-module is further configured to merge the first position behavior data into the sample data, and use the final value as a label of the first position behavior data to participate in training the decision tree.
In a third aspect of the disclosed embodiment, an electronic device is also provided. The electronic device includes one or more processors, and one or more memories. The one or more memories are for storing one or more programs. Wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the above-described method.
In a fourth aspect of the embodiments of the present disclosure, there is also provided a computer-readable storage medium having stored thereon executable instructions, which when executed by a processor, cause the processor to perform the above-mentioned method.
In a fifth aspect of the embodiments of the present disclosure, there is also provided a computer program product comprising a computer program which, when executed by a processor, implements the above method.
One or more of the above-described embodiments may provide the following advantages or benefits: different evaluation rules can be set for different posts and different combinations of human resource evaluation indexes, different decision trees are correspondingly created, and a human resource evaluation model is obtained through training of the decision trees, so that the human resource evaluation model can evaluate the performance of workers in a mode close to the historical true evaluation level, and diversification, refinement, automation and intellectualization of human resource evaluation can be realized.
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The foregoing and other objects, features and advantages of the disclosure will be apparent from the following description of embodiments of the disclosure, which proceeds with reference to the accompanying drawings, in which:
FIG. 1 schematically illustrates an application scenario diagram of a method, apparatus, device, medium and program product for human resources evaluation according to an embodiment of the present disclosure;
FIG. 2 schematically illustrates a flow chart of a method for human resources assessment in accordance with an embodiment of the present disclosure;
FIG. 3 is a schematic diagram illustrating different decision trees constructed for different human resource assessment indicators on the same post according to an embodiment of the disclosure;
FIG. 4 is a flow chart schematically illustrating evaluation of a human resources evaluation index of a worker on a corresponding post using a human resources evaluation model according to an embodiment of the present disclosure;
FIG. 5 schematically illustrates a block diagram of an apparatus for human resources assessment in accordance with an embodiment of the present disclosure;
FIG. 6 schematically illustrates a logical structure of an apparatus for human resources assessment according to another embodiment of the present disclosure; and
FIG. 7 schematically illustrates a block diagram of an electronic device suitable for implementing a method for human resources assessment in accordance with an embodiment of the present disclosure.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is illustrative only and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.
Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).
In this document, it is to be understood that any number of elements in the specification and drawings is to be considered exemplary rather than limiting, and that any nomenclature (e.g., first, second) is used for distinction only, and not in any limiting sense.
Embodiments of the present disclosure provide a method, apparatus, device, medium, and program product for human resource assessment. Wherein, the method can comprise the following steps: firstly, setting at least one evaluation rule, wherein each evaluation rule is used for expressing the mapping relation between the job performance data of one post and one human resource evaluation index; then constructing a decision tree based on each evaluation rule; then, taking the post performance data of the post related to the evaluation rule in the historical data as sample data, and taking the human resource evaluation index related to the evaluation rule in the historical data as a label of the sample data, and training the decision tree; the human resource evaluation model corresponds to the positions related to the evaluation rules and the human resource evaluation indexes; and finally, evaluating the performance of the corresponding human resource evaluation index of the staff on the corresponding post by using the human resource evaluation model.
According to the embodiment of the disclosure, different evaluation rules can be set for different posts and different combinations of human resource evaluation indexes, different decision trees are correspondingly created, and a human resource evaluation model is obtained through training of the decision trees, so that the human resource evaluation model can evaluate the performance of workers in a mode close to the historical true evaluation level, and diversification, refinement, automation and intellectualization of human resource evaluation can be realized.
According to the embodiment of the disclosure, the evaluation rules of the post job performance data and the corresponding human resource evaluation indexes can be set according to the post service characteristics (such as short-term outsourcing or residency, fixed-point office or remote office, fixed service content or cross-department circulation cooperation) and the service content, so that the finally trained human resource evaluation model can evaluate the performance of the staff in the post more objectively and pertinently.
According to the embodiment of the disclosure, the performance of the staff in the post can be dynamically evaluated in real time. For example, after a project is finished, the performance of a certain worker in the project can be evaluated by using a human resource evaluation model, or when a certain worker is a transient borrower, the performance of the worker during the borrowing period can be evaluated by using the human resource evaluation model corresponding to the borrowing position according to the job performance data of the worker during the transient borrowing period; or when a person leaves, the performance of the person leaving from the last evaluation deadline to the time of leaving can be evaluated by using the human resource evaluation model. In this way, the embodiment of the disclosure can realize real-time and dynamic human resource evaluation according to the actual work content, the actual work duration or the actual evaluation requirement of the staff, and effectively improve the efficiency and the accuracy of the human resource evaluation.
It should be noted that the method, apparatus, device, medium, and program product for human resource evaluation provided by the embodiments of the present disclosure may be used in the financial field, and may also be used in any field other than the financial field, and the application field is not limited by the present disclosure.
In the technical scheme of the disclosure, the acquisition, storage, application and the like of the related personal information all accord with the regulations of related laws and regulations, necessary security measures are taken, and the customs of the public order is not violated.
FIG. 1 schematically illustrates an application scenario diagram of a method, apparatus, device, medium and program product for human resources evaluation according to an embodiment of the present disclosure. It should be noted that fig. 1 is only an example of a system architecture to which the embodiments of the present disclosure may be applied to help those skilled in the art understand the technical content of the present disclosure, and does not mean that the embodiments of the present disclosure may not be applied to other devices, systems, environments or scenarios.
As shown in fig. 1, the application scenario 100 according to this embodiment may include at least one terminal device (three are shown in the figure, terminal devices 101, 102, 103), a network 104, and a server 105. The network 104 is used to provide communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
A human resource manager (hereinafter, referred to as manager) can use the terminal devices 101, 102, 103 to interact with the server 105 through the network 104 to receive or transmit messages and the like.
The server 105 may be a server that provides various human resources management services. A decision tree model may be deployed in the server 105.
According to an embodiment of the present disclosure, a manager may compose an evaluation rule using the terminal devices 101, 102, 103 and upload to the server 105. The server 105 may create a decision tree according to the evaluation rule uploaded by the administrator using the terminal devices 101, 102, 103, train the decision tree to obtain a human resource evaluation model, evaluate performance of the staff at the corresponding positions on the corresponding human resource evaluation indexes by using the human resource evaluation model, and feed back the evaluation result to the terminal devices 101, 102, 103.
It should be noted that the method for human resources evaluation provided by the embodiments of the present disclosure may be generally executed by the server 105. Accordingly, the apparatus, device, medium, and program product for human resources evaluation provided by the embodiments of the present disclosure may be generally disposed in the server 105. The method for human resource evaluation provided by the embodiment of the present disclosure may also be performed by a server or a server cluster which is different from the server 105 and can communicate with the terminal devices 101, 102, 103 and/or the server 105. Accordingly, the apparatus, device, medium and program product for human resources evaluation provided by the embodiments of the present disclosure may also be disposed in a server or a server cluster different from the server 105 and capable of communicating with the terminal devices 101, 102, 103 and/or the server 105.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
The method for human resources evaluation of the disclosed embodiment will be described in detail below through fig. 2 to 4 based on the scenario described in fig. 1.
FIG. 2 schematically illustrates a flow chart of a method for human resources assessment in accordance with an embodiment of the present disclosure.
As shown in fig. 2, the method for human resources evaluation of this embodiment may include operations S210 to S240.
First, in operation S210, at least one evaluation rule is set, where each evaluation rule is used to represent a mapping relationship between job performance data of one post and a human resource evaluation index.
Then, in operation S220, a decision tree is constructed based on each of the evaluation rules.
The human resource evaluation index in the embodiment of the present disclosure may be an index for measuring performance of a worker in any dimension during work, such as a work contribution index, a salary cost index, a responsibility index, and the like. The work contribution degree index is used for measuring the output contribution of the working staff to the work of the position within a period of time. The compensation cost index is used to measure the cost invested in a certain post of staff over a period of time. The responsibility plays an index for measuring the degree of full time of the work of the staff on the post in a period of time and the like.
According to the embodiment of the disclosure, the administrator can set an evaluation rule corresponding to a human resource evaluation index at a certain position in a period of time through the terminal devices 101, 102, 103.
The evaluation rule may comprise both quantitative and qualitative portions. The evaluation rules may include the composition of the position data for quantitative evaluation, the source of each job data, and the calculation rules (or calculation modes) of the workload of each job in the calculation process of the human resource evaluation index. Meanwhile, the evaluation rule may also include an evaluator of various types of information for qualitative evaluation, and a calculation rule (or a calculation expression) of the qualitative evaluation of each job in the calculation process of the human resource evaluation index.
For example, X1、X2...XkRespectively represent the job-performing behavior data of a certain post, and belong to an independent variable, beta, influencing the evaluation index of human resources in the evaluation rule1、β2...βkRepresents the degree of influence of the independent variable on the dependent variable Y (i.e., the human resource evaluation index), and the influence of the independent variable on the dependent variable may be linear or non-linear. Thus, in some embodiments, the evaluation rule may be represented by a linear expression or a non-linear expression.
The expression or parameters of the evaluation rule can realize the flexibility and customized configuration of the parameters according to the general models and historical data in the industry and the historical data of the human resources of the enterprises. For example, in one embodiment, an evaluation rule may be represented by equation (1).
Figure BDA0003328206780000081
Of course, it is understood that equation (1) is merely an example. In practice, different algorithms can be recommended to the human resource evaluation index according to the service characteristics of different posts. For example, the evaluation rules when a business architect evaluates a human resource evaluation index (e.g., a work contribution degree) are exemplarily illustrated in the following tables 1a to 1c, where the evaluation rules are different from the formula (1). It should be noted that tables 1a to 1c are an entirety, and are shown by being split into three tables for clarity of illustration, and the entirety of the tables illustrates an evaluation rule for evaluating a human resource evaluation index, which is a work contribution degree, by a business architect.
Reference is made to tables 1 a-1 c, wherein exemplary business architects' job performance data X1、X2...XkAnd a parameter of degree of influence β in the evaluation rule1、β2...βkIs set. When the work contribution index evaluation is performed on the service architect in tables 1a to 1c, different levels can be divided according to the work content of the service architect, and the hierarchical evaluation can be performed. And can adopt the absolute degree of contribution when evaluating the work contribution degree of the business architect in each levelEvaluation rules are set for various algorithms such as a value evaluation method and a step evaluation method. In the algorithm calculation method columns in tables 1a to 1c, a represents an absolute value evaluation method, and S represents a step evaluation method.
For example, in table 1a, the work calculation rules of the business architect that have been entered automatically identify all work items and raw data at each level, and obtain corresponding independent variable values for the matching calculation method. For example, learning and training are identified as first-level work items, and their corresponding second-level work items include both learning and examination items. Setting the learning calculation rule as class hour > 50 and full score 8; otherwise, it is 0 point. Therefore, an absolute value evaluation method is recommended to obtain a learning work item value of X11, and an index calculation formula of a second level is a 11X 11. And by analogy, obtaining the index calculation formula of each level.
Therefore, according to tables 1a to 1c, the evaluation rule when the business architect evaluates according to the work contribution index can be expressed as formula (2):
Figure BDA0003328206780000091
next, in operation S230, a decision tree is trained by using the position performing behavior data related to an evaluation rule in the historical data as sample data and using the human resource evaluation index related to the evaluation rule in the historical data as a label of the sample data, so as to obtain a human resource evaluation model.
For example, a large amount of sample data may be obtained by obtaining values of X11, X12, X311, and the like of a large number of business architects within a certain period of time from the work items of each hierarchy in tables 1a to 1c, and a human resource evaluation model that evaluates the work contribution of the business architects may be obtained by training a decision tree with the value of the corresponding work contribution index of each business architect within the certain period of time as a label of the corresponding sample data.
Figure BDA0003328206780000101
TABLE 1a
Figure BDA0003328206780000102
TABLE 1b
Figure BDA0003328206780000103
TABLE 1c
According to the embodiment of the disclosure, the acquisition of the job performance data can be automatically realized according to the evaluation rule. For example, in the aspect of quantitative data, various original data in the job-related behavior data can be acquired in real time through API interfaces of various information systems; in the aspect of qualitative data, managers can be automatically reminded to conduct qualitative evaluation on the work of workers, and then real-time stepped evaluation data can be captured.
When the decision tree is trained, the model training effect can be evaluated by performing feature extraction and data cleaning on industrial general historical data and enterprise human resource historical data (including work types, work sub-items, quantitative data of each work, qualitative evaluation data of each work, and capturing and classifying statistics on original historical data according to an evaluation rule), then performing repeated training and iterative correction on the decision tree (such as a GBDT gradient enhancement decision tree), and using the accuracy as a standard. In the captured historical data, 80% of the data can be randomly extracted each time to serve as a training set, and the rest 20% of the data can serve as a verification set. And training the decision tree through the GBDT gradient enhancement decision tree to obtain a human resource evaluation model for evaluating the staff in the corresponding post according to the corresponding human resource evaluation index.
Finally, in operation S240, the performance of the corresponding staff on the post on the corresponding human resource evaluation index is evaluated by using the human resource evaluation model.
According to the embodiment of the disclosure, different evaluation rules (for example, different contents or sources of the job performance data or different algorithms of the evaluation rules) can be set for the same human resource evaluation index in different positions, so that corresponding human resource evaluation models can be trained independently. Therefore, the pertinence and the intellectualization of the human resource evaluation of different posts are realized.
Or, different evaluation rules can be set for different human resource evaluation indexes in the same post, and correspondingly, different decision trees can be constructed for training. In this case, in an embodiment, when N evaluation rules are respectively used to represent mapping relationships between the employment behavior data of the same post and N human resource evaluation indexes, the employment behavior data of the N evaluation rules may be set to be the same but with different algorithms, where N is an integer greater than or equal to 2. Therefore, sharing of the job-performing behavior data can be facilitated, repeated collection of the original data is reduced, and the use efficiency of the original data is improved.
FIG. 3 is a schematic diagram illustrating different decision trees constructed for different human resource evaluation indexes in the same position according to an embodiment of the disclosure.
As shown in fig. 3, when different decision trees are constructed for different human resource evaluation indexes in the same position, when the job performance data in the evaluation rules corresponding to the different human resource evaluation indexes in the same position are the same, the constructed different decision trees may share the input data. For example, in fig. 3, the decision tree 1 and the decision tree 2 may be used to process the job performance data of the same worker, so as to obtain the work contribution index and the compensation cost index of the worker.
By sharing the input data, the utilization rate of the input data used by the decision tree 1 and the decision tree 2 in the training stage and the prediction stage after the training is finished (namely, the input data are used as corresponding human resource evaluation models) is improved, and the human resource evaluation efficiency is improved.
FIG. 4 is a flow chart schematically illustrating the evaluation of the human resources evaluation index of the staff on the corresponding post by using the human resources evaluation model in operation S240 according to the embodiment of the disclosure.
As shown in fig. 4, operation S240 may include operations S401 to S403 according to an embodiment of the present disclosure.
In operation S401, first job performance data of a first worker is acquired based on job performance data of a post related to an evaluation rule corresponding to the human resource evaluation model. For example, when the first worker is a business architect, values of the work items of each hierarchy may be obtained according to tables 1a to 1 c.
In operation S402, the first employment behavior data is processed by using the human resource evaluation model, and a first index value of the first staff member on the corresponding human resource evaluation index is output.
The new job performance data can be analyzed in real time, and index values of the first worker in the corresponding one or more human resource evaluation indexes are evaluated by combining with one or more human resource evaluation models obtained newly. In addition, the output result of the human resource evaluation model can be dynamically displayed, and the management personnel can be provided with real-time inquiry authority.
Still further, in operation S403, a final value of the first worker on the corresponding human resources evaluation index may be output based on the adjustment or confirmation of the first index value by the manager. The manager can manually adjust and confirm the human resource evaluation index value of the worker in real time, and then output the final value of the current human resource evaluation index of the worker as the work performance.
According to an embodiment of the present disclosure, the first job performance data may be merged into the sample data after operation S403, and the final value of the human resource evaluation index may be used as a label of the first job performance data to participate in training the decision tree. Therefore, the manually adjusted and confirmed data is used as historical data to further repeat training and iterative correction of the human resource evaluation model, and a closed flow loop is formed.
Based on the method for human resource evaluation described in the above embodiments, the present disclosure also provides an apparatus for human resource evaluation. The apparatus will be described in detail below with reference to fig. 5 and 6.
FIG. 5 schematically illustrates a block diagram of an apparatus 500 for human resources assessment in accordance with an embodiment of the present disclosure.
As shown in fig. 5, the apparatus 500 for human resources evaluation according to some embodiments of the present disclosure may include an evaluation rule entering and adjusting module 510, and a data analyzing and intelligent algorithm module 520. The data analysis and intelligent algorithm module 520 includes a decision tree construction sub-module 521, a training sub-module 522, and an evaluation sub-module 523. According to other embodiments of the present disclosure, the apparatus 500 may further include a data collecting and capturing module 530 and/or a query and management module 540.
The evaluation rule entering and adjusting module 510 is configured to set at least one evaluation rule, where each evaluation rule is used to represent a mapping relationship between the job performance data of one post and one human resource evaluation index. In one embodiment, the evaluation rule entry and adjustment module 510 may be used, for example, to perform operation S210.
The decision tree construction sub-module 521 is used for constructing a decision tree based on each evaluation rule. In one embodiment, decision tree building submodule 521 may be configured to perform operation S220.
The training submodule 522 is configured to train a decision tree by using the position performing behavior data related to the evaluation rule in the historical data as sample data, and using the human resource evaluation index related to the evaluation rule in the historical data as a label of the sample data, so as to obtain a human resource evaluation model. In one embodiment, the training submodule 522 may be configured to perform operation S230.
The evaluation submodule 523 is configured to evaluate, by using the human resource evaluation model, performance of the corresponding human resource evaluation index of the staff on the corresponding post. In one embodiment, the evaluation sub-module 523 may be used to perform operation S240.
The data collecting and capturing module 530 is configured to obtain first job performance data of the first worker based on job performance data of a post involved in an evaluation rule corresponding to the human resource evaluation model. Correspondingly, the evaluation sub-module 523 is further configured to process the first employment behavior data by using the human resource evaluation model, and output a first index value of the first worker on the human resource evaluation index corresponding to the human resource evaluation model. In one embodiment, the data collection and capture module 530 may be configured to perform operation S401, and the evaluation sub-module 523 may be configured to perform operation S402 accordingly.
The query management module 540 is configured to output a final value of the human resource evaluation index based on adjustment or confirmation of the first index value by the administrator. In one embodiment, the query management module 540 may be configured to perform operation S403.
In addition, the training sub-module 522 is further configured to incorporate the first position behavior data into the sample data, and participate in the training of the decision tree with the final value as a label of the first position behavior data.
According to an embodiment of the present disclosure, the apparatus 500 may be used to implement the methods described with reference to fig. 2 to fig. 4, and the foregoing description is specifically referred to, and is not repeated here.
According to the embodiment of the present disclosure, any of the evaluation rule entering and adjusting module 510, the data analyzing and intelligent algorithm module 520, the data collecting and capturing module 530, or the query and management module 540 may be combined into one module to be implemented, or any one of them may be split into a plurality of modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of the other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the evaluation rule entering and adjusting module 510, the data analyzing and intelligent algorithm module 520, the data collecting and grabbing module 530, or the query and management module 540 may be implemented at least partially as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented by hardware or firmware in any other reasonable manner of integrating or packaging a circuit, or implemented by any one of three implementations of software, hardware, and firmware, or by a suitable combination of any several of them. Alternatively, at least one of the evaluation rules entry and adjustment module 510, the data analysis and intelligent algorithm module 520, the data collection and crawling module 530 or the query and management module 540 may be at least partially implemented as a computer program module that, when executed, may perform a corresponding function.
FIG. 6 schematically shows a logical structure of an apparatus 600 for human resources assessment according to another embodiment of the present disclosure.
As shown in fig. 6, the logical structure of the apparatus 600 for human resources evaluation can be divided into three major parts, namely rule management, data processing, and information output. The evaluation rule entry and adjustment module 610 and the query and management module 640 are used for rule management. The data acquisition and capture module 630 and the data analysis and intelligent algorithm module 620 are used for data processing. The work performance output module 650 is used for information output. The apparatus 600 is a specific embodiment of the apparatus 500, wherein the evaluation rule entering and adjusting module 610, the data analyzing and intelligent algorithm module 620, the data collecting and capturing module 630 and the query and management module 640 are respectively corresponding to specific embodiments of the evaluation rule entering and adjusting module 510, the data analyzing and intelligent algorithm module 520, the data collecting and capturing module 530 and the query and management module 540.
Table 2 details the input, output, and processing logic of each module in the apparatus 600, wherein the human resources evaluation index is exemplified as the work contribution index.
Figure BDA0003328206780000151
Table 2 details of the modules
The workflow of the apparatus 600 includes: the evaluation rule inputting and adjusting module 640 determines a human resource evaluation rule, the data collecting and capturing module 630 acquires work contribution original data, the data analyzing and intelligent algorithm module 620 trains a human resource evaluation model and calculates human resource evaluation indexes, and the work performance output module 650 outputs the work performance of the personnel.
The following description will more clearly see the flow processing process of the device 600 by performing the function and flow combing according to the actual working example of the device 600.
The manager determines the evaluation rule of the work contribution index of a certain post in the period, and calls the evaluation rule recording and adjusting module 610.
Acquiring original data (namely, job-related behavior data) for calculating the contribution degree of the job, and calling a data acquisition and capture module 630;
and calling a data analysis and intelligent algorithm module 620, training a decision tree constructed based on the evaluation rule through a GBDT gradient enhancement decision tree according to historical data, and calculating the human resource evaluation indexes of the external engaging personnel and the internal personnel of the enterprise by using a human resource evaluation model obtained by training.
The data analysis and intelligent algorithm module 620, which may also implement a distance fix for the model bias epsilon (e.g., see equation (1)); and (4) carrying out deviation correction and correction on the whole model by relying on stock performance data.
The manager can inquire the calculation result in real time, manually adjust and confirm the contribution and performance result of the employee according to the actual situation, and call the inquiry and management module 640;
the apparatus 600 outputs the annual or quarterly work performance of the person and calls the work performance output module 650.
The following will further describe the processing logic of each module in the apparatus 600 in practice by taking a typical example of how to evaluate the work contribution and compensation cost of the cross-department business architect in an enterprise together with tables 1a to 1 c.
Firstly, the human resource department decomposes sub-targets (such as asset construction, asset application, original value output and architecture landform) of the business architect according to the annual work target and the key work task of the business architect, and each sub-target is provided with a qualitative evaluation standard and a quantitative index evaluation rule, and the contribution ratio of qualitative work and quantitative work is determined. The human resource manager enters the rule in the evaluation rule entry and adjustment module 610. If the evaluation rule changes in the present work cycle, the evaluation rule may be adjusted in the evaluation rule entry and adjustment module 610.
The apparatus 600 then interacts with the associated work system through the API interface according to the decomposed work subtasks, and obtains the original data of work contribution (i.e., job performance data) related to each subtask. For example, in the asset application work subtask, the data acquisition and capture module 630 acquires data information for approving the service architecture analysis by the service architect during the project establishment, the requirement scheme review and the requirement transfer through the linkage service research and development management system; the data collection and capture module 630 automatically reminds the manager to perform qualitative evaluation on the asset application work of the business architect, and obtains real-time stepped evaluation data.
Then, the data analysis and intelligent algorithm module 620 performs feature extraction and data cleaning on the industry general historical data and the enterprise human resource historical data (including work types, work sub-items, each work quantitative data and each work qualitative evaluation data, and can perform grabbing and classification statistics on the original historical data according to the human resource evaluation rule), performs repeated training and iterative correction on the initial model established based on the evaluation rule through the GBDT gradient enhancement decision tree, and uses the accuracy as a standard to obtain the human resource evaluation model. When the work contribution and the salary cost of the business architect need to be evaluated respectively, the data analysis and intelligent algorithm module 620 can be used for training two decision trees, the two decision trees can share the input job-performing behavior data, and the work contribution and the salary cost of the business architect can be displayed to the human resource manager in one interface.
The device 600 may further analyze the new raw data, and according to the entered evaluation rule, combine with the latest human resource evaluation model, calculate and dynamically display the work contribution and compensation cost evaluation of the business architect in real time.
In the device 600, a decision tree is constructed according to parameters set in the evaluation rules or recommended according to the working types, the contribution of each level of work is calculated through a GBDT algorithm, and the parameters also support real-time adjustment and flexible matching of managers, so that customized measurement of the contribution of work is realized.
The output of the human resource evaluation model is adjusted and confirmed by the manager, for example, the device 600 may output the work contribution or compensation cost of the business architect at the end of each season or year. The manager can examine the evaluation rule set at the initial period according to the performance distribution condition, so as to adjust the evaluation rule of the next period. Meanwhile, the work contribution and performance results of each period are used as stock data, and continuous training and correction are carried out on the algorithm model by combining the adjustment condition of a manager. For example, the number of times, the adjustment range, the work type and the like of manual adjustment of a manager of the contribution degree of a certain work are used as input elements of the training model, and accuracy is provided for a new algorithm model.
In this way, the apparatus 600 may perform training and modification iteration of the algorithm model on the historical data through the machine learning technique by using the real-time work contribution quantitative data and the real-time stepwise evaluation data (e.g., qualitative evaluation data), thereby achieving real-time calculation of human resource contribution and/or salary cost, and may calculate the performance cost of the external personnel and the internal personnel in real time by combining the work contribution and the salary cost accounting mechanism. Therefore, the work contribution and the performance cost of the personnel can be dynamically displayed in real time, and the management personnel can be supported to inquire the performance calculation result in real time. And at the end of the work period, the annual or quarterly work performance of the personnel is output in combination with a traditional management evaluation system.
Therefore, the device 600 can be used as a performance evaluation tool for technical personnel of internal and external packages of enterprises and members of a cross-department matrix management team, and does not depend on traditional human resource evaluation modes such as manual evaluation and static quantitative statistics, so that the human resource evaluation efficiency of large enterprises is improved.
FIG. 7 schematically illustrates a block diagram of an electronic device suitable for implementing a method for human resources assessment in accordance with an embodiment of the present disclosure.
As shown in fig. 7, an electronic device 700 according to an embodiment of the present disclosure includes a processor 701, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)702 or a program loaded from a storage section 708 into a Random Access Memory (RAM) 703. The processor 701 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or associated chipset, and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), among others. The processor 701 may also include on-board memory for caching purposes. The processor 701 may comprise a single processing unit or a plurality of processing units for performing the different actions of the method flows according to embodiments of the present disclosure.
In the RAM 703, various programs and data necessary for the operation of the electronic apparatus 700 are stored. The processor 701, the ROM 702, and the RAM 703 are connected to each other by a bus 704. The processor 701 performs various operations of the method flows according to the embodiments of the present disclosure by executing programs in the ROM 702 and/or the RAM 703. It is noted that the programs may also be stored in one or more memories other than the ROM 702 and RAM 703. The processor 701 may also perform various operations of method flows according to embodiments of the present disclosure by executing programs stored in the one or more memories.
Electronic device 700 may also include input/output (I/O) interface 705, which input/output (I/O) interface 705 is also connected to bus 704, according to an embodiment of the present disclosure. The electronic device 700 may also include one or more of the following components connected to the I/O interface 705: an input portion 706 including a keyboard, a mouse, and the like; an output section 707 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 708 including a hard disk and the like; and a communication section 709 including a network interface card such as a LAN card, a modem, or the like. The communication section 709 performs communication processing via a network such as the internet. A drive 710 is also connected to the I/O interface 705 as needed. A removable medium 711 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 710 as necessary, so that a computer program read out therefrom is mounted into the storage section 708 as necessary.
The present disclosure also provides a computer-readable storage medium, which may be contained in the apparatus/device/system described in the above embodiments; or may exist separately and not be assembled into the device/apparatus/system. The computer-readable storage medium carries one or more programs which, when executed, implement the method according to an embodiment of the disclosure.
According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example but is not limited to: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. For example, according to embodiments of the present disclosure, a computer-readable storage medium may include the ROM 702 and/or the RAM 703 and/or one or more memories other than the ROM 702 and the RAM 703 described above.
Embodiments of the present disclosure also include a computer program product comprising a computer program containing program code for performing the method illustrated in the flow chart. When the computer program product runs in a computer system, the program code is used for causing the computer system to realize the method provided by the embodiment of the disclosure.
The computer program performs the above-described functions defined in the system/apparatus of the embodiments of the present disclosure when executed by the processor 701. The systems, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.
In one embodiment, the computer program may be hosted on a tangible storage medium such as an optical storage device, a magnetic storage device, or the like. In another embodiment, the computer program may also be transmitted in the form of a signal on a network medium, distributed, downloaded and installed via the communication section 709, and/or installed from the removable medium 711. The computer program containing program code may be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.
In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 709, and/or installed from the removable medium 711. The computer program, when executed by the processor 701, performs the above-described functions defined in the system of the embodiment of the present disclosure. The systems, devices, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.
In accordance with embodiments of the present disclosure, program code for executing computer programs provided by embodiments of the present disclosure may be written in any combination of one or more programming languages, and in particular, these computer programs may be implemented using high level procedural and/or object oriented programming languages, and/or assembly/machine languages. The programming language includes, but is not limited to, programming languages such as Java, C + +, python, the "C" language, or the like. The program code may execute entirely on the user computing device, partly on the user device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Those skilled in the art will appreciate that various combinations and/or combinations of features recited in the various embodiments and/or claims of the present disclosure can be made, even if such combinations or combinations are not expressly recited in the present disclosure. In particular, various combinations and/or combinations of the features recited in the various embodiments and/or claims of the present disclosure may be made without departing from the spirit or teaching of the present disclosure. All such combinations and/or associations are within the scope of the present disclosure.
The embodiments of the present disclosure have been described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described separately above, this does not mean that the measures in the embodiments cannot be used in advantageous combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be devised by those skilled in the art without departing from the scope of the present disclosure, and such alternatives and modifications are intended to be within the scope of the present disclosure.

Claims (11)

1. A method for human resources evaluation, comprising:
setting at least one evaluation rule, wherein each evaluation rule is used for expressing the mapping relation between the job performance data of one post and one human resource evaluation index;
constructing a decision tree based on each evaluation rule;
training the decision tree by taking the job performance data of the posts related to the evaluation rules in the historical data as sample data and taking the human resource evaluation indexes related to the evaluation rules in the historical data as labels of the sample data to obtain a human resource evaluation model; and
and evaluating the performance of the staff on the corresponding post on the corresponding human resource evaluation index by using the human resource evaluation model.
2. The method of claim 1, wherein the setting at least one evaluation rule comprises:
when the N evaluation rules are respectively used for representing the mapping relation between the position-performing behavior data and N human resource evaluation indexes, the position-performing behavior data in the N evaluation rules are set to be the same but with different algorithms, wherein N is an integer greater than or equal to 2.
3. The method of claim 2, wherein said constructing a decision tree based on each of said evaluation rules comprises:
and constructing N decision trees based on the N evaluation rules, wherein the N decision trees share input data.
4. The method of claim 2, wherein the N human resource evaluation metrics include a work contribution metric and a compensation cost metric.
5. The method according to any one of claims 1 to 4, wherein the evaluating the performance of the staff members on the corresponding posts on the corresponding human resource evaluation indexes by using the human resource evaluation model comprises:
acquiring first job performance data of a first worker based on the job performance data of the post related to the evaluation rule corresponding to the human resource evaluation model; and
and processing the first job performance data by using the human resource evaluation model, and outputting a first index value of the first worker on the corresponding human resource evaluation index.
6. The method of claim 5, wherein the evaluating the performance of the staff members on the corresponding posts on the corresponding human resources evaluation indicators using the human resources evaluation model further comprises:
and outputting the final value of the corresponding human resource evaluation index of the first worker based on the adjustment or confirmation of the first index value by the manager.
7. The method of claim 6, wherein the method further comprises:
and merging the first job-related behavior data into the sample data, and taking the final value as a label of the first job-related behavior data to participate in the training of the decision tree.
8. An apparatus for human resources evaluation, comprising:
the evaluation rule recording and adjusting module is used for setting at least one evaluation rule, and each evaluation rule is used for expressing the mapping relation between the job performance data of one post and one human resource evaluation index;
the data analysis and intelligent algorithm module comprises:
a decision tree construction submodule for constructing a decision tree based on each of the evaluation rules;
the training submodule is used for training the decision tree by taking the position corresponding to the evaluation rule in the historical data as sample data and taking the human resource evaluation index corresponding to the evaluation rule in the historical data as a label of the sample data to obtain a human resource evaluation model; and
and the evaluation submodule is used for evaluating the performance of the corresponding staff on the post on the corresponding human resource evaluation index by using the human resource evaluation model.
9. An electronic device, comprising:
one or more processors;
one or more memories for storing one or more programs,
wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the method of any of claims 1-7.
10. A computer readable storage medium having stored thereon executable instructions which, when executed by a processor, cause the processor to perform the method of any one of claims 1 to 7.
11. A computer program product comprising a computer program which, when executed by a processor, implements a method according to any one of claims 1 to 7.
CN202111279738.2A 2021-10-29 2021-10-29 Method, device, equipment and medium for human resource evaluation Pending CN113962664A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116307340A (en) * 2022-11-28 2023-06-23 广州合道信息科技有限公司 Post matching management platform, electronic equipment and storage medium

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116307340A (en) * 2022-11-28 2023-06-23 广州合道信息科技有限公司 Post matching management platform, electronic equipment and storage medium
CN116307340B (en) * 2022-11-28 2023-11-14 广州合道信息科技有限公司 Post matching management platform, electronic equipment and storage medium

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