CN114399158A - Analysis method for employee behavior and ability dimension - Google Patents

Analysis method for employee behavior and ability dimension Download PDF

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CN114399158A
CN114399158A CN202111511854.2A CN202111511854A CN114399158A CN 114399158 A CN114399158 A CN 114399158A CN 202111511854 A CN202111511854 A CN 202111511854A CN 114399158 A CN114399158 A CN 114399158A
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behavior
formula
employee behavior
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郑维
郝立宏
孙思
曾芮
刘涛
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Guodian Dadu River Hydropower Development Co Ltd
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Abstract

The invention relates to the technical field of software development, in particular to an employee behavior and ability dimension analysis method, which comprises the following steps: s1: setting an employee behavior data acquisition model, and acquiring employee behavior data according to the employee behavior data acquisition model; s2: storing the employee behavior data into a database; s3: if the employee behavior analysis message is acquired, acquiring employee data to be acquired from the employee behavior analysis; s4: and acquiring the employee behavior data from the database according to the employee data to be acquired, and sending the employee behavior data to a management terminal. Furthermore, management personnel can analyze the behaviors of the staff according to actual requirements, performance contribution of the staff is assessed and evaluated more objectively and fairly, and enabling of big data to human resource management business is achieved.

Description

Analysis method for employee behavior and ability dimension
Technical Field
The invention relates to an analysis method for employee behavior and ability dimensions.
Background
The traditional human resource management faces the problems of data island, staff evaluation subjectivity and the like. The large-scale central enterprise is complex in state, the water and electricity industry human resource data are heterogeneous in multi-source, four kinds of data such as organizations, personnel information, salary management, training and the like are dispersedly stored in a group human resource information system, an enterprise annuity system, a social security system, a public accumulation system and the like to form a data isolated island; the traditional employee competency evaluation is based on subjective judgment, which causes main deviation of human resource management, and the reason for the deviation may be due to differences of enterprise administrator competence and management fineness, or due to subjective differences of matching degree identification between human resource post estimation and employee competency.
With the deep advance of intelligent enterprise construction, enterprises show a trend of flexible management, the organization form gradually tends to be flexible, and the working relation network of employees is complex, which provides more detailed and more intelligent development requirements for human resource management, but the prior art cannot well process the requirements.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide an analysis method for employee behavior and ability dimensions.
The purpose of the invention is realized by the following technical scheme:
an analysis method for employee behavior and ability dimensions is characterized in that: the method comprises the following steps:
s1: building a staff behavior data acquisition model, and acquiring staff behavior data according to the staff behavior data acquisition model;
s2: storing the employee behavior data into a database;
s3: if the employee behavior analysis message is acquired, acquiring employee data to be acquired from the employee behavior analysis;
s4: and acquiring the employee behavior data from the database according to the employee data to be acquired, and sending the employee behavior data to a management terminal.
Further, step S1 includes:
s11: acquiring data acquisition latitude data, and setting the employee behavior data acquisition model according to the data acquisition latitude data;
s12: and acquiring corresponding employee behavior data by using the data acquisition latitude data in the employee behavior data acquisition model.
Further, step S3 includes:
s31: acquiring a data acquisition type from the employee behavior analysis message;
s32: and acquiring the employee data to be acquired according to the data acquisition type.
Further, step S4 includes:
s41: classifying the employee behavior data according to the employee behavior analysis message to obtain data to be analyzed;
s42: and sending the data to be analyzed to the management terminal.
Further, the step S1 further includes a method for establishing an employee group tag library, where:
s101: establishing personal portrait and organization portrait of employees through data acquisition;
s102: and (3) building an employee grouping label library through employee personal portrait and organization portrait: the formula is set up as follows:
the formula I is as follows:
Figure BDA0003396036820000021
the formula II is as follows:
Figure BDA0003396036820000022
the formula III is as follows:
Figure BDA0003396036820000031
in the formula: x represents a data point in the anatomical representation; μ represents a centroid in the tissue representation; n represents the number of employee portraits; i represents each feature constituting x;
s103: the contour coefficient S is defined by the tissue image, which is formulated as follows:
the formula four is as follows:
Figure BDA0003396036820000032
in the formula: a represents the similarity between the employee portrait data point and other data points in the organization portrait of the employee, and is equal to the average distance between the employee portrait data point and all other employee portrait data points in the same organization portrait; b represents the similarity of the employee representation data points to employee representation data points in other organizational representations equal to the average distance between the sample and all points in the next closest organizational representation;
and defining the value range of the contour coefficient as (-1, 1) according to the formula IV.
Further, the method also comprises an employee evaluation model establishing method, and comprises the following steps: establishing an employee evaluation model based on the organization sketch, wherein the formula is as follows:
the formula five is as follows:
Figure BDA0003396036820000033
in the formula, T (x; theta)m) Representing a decision tree composed of the organization images; thetamParameters of the decision tree; m is the tree formed by the employee portrait;
the lifting tree algorithm adopts a forward distribution algorithm; determining an initial lifting tree f0(x) The model at step m is 0:
formula six: f. ofm(x)＝fm-1(x)+T(x；Θm)；
In the formula (f)m-1(x) For the current model, the parameters Θ of the next decision tree are determined by empirical risk minimizationm：
The formula seven:
Figure BDA0003396036820000034
and substituting the last prediction result into the gradient to obtain the training data of the round, and completing the establishment of the employee evaluation model.
An analysis system for employee behavior and competency dimensions, comprising:
the model acquisition module is used for setting an employee behavior data acquisition model and acquiring employee behavior data according to the employee behavior data acquisition model;
the storage module is used for storing the employee behavior data into a database;
the data acquisition module is used for acquiring employee data to be acquired from employee behavior analysis if the employee behavior analysis message is acquired;
and the data sending module is used for obtaining the employee behavior data from the database according to the employee data to be obtained and sending the employee behavior data to a management terminal.
Further, the model acquisition module comprises:
the latitude setting submodule is used for acquiring data acquisition latitude data and setting the employee behavior data acquisition model according to the data acquisition latitude data;
and the model acquisition submodule is used for acquiring latitude data by using the data in the employee behavior data acquisition model to acquire corresponding employee behavior data.
The invention has the beneficial effects that:
1. the employee behavior data acquisition model is preset, so that employee behavior data can be conveniently acquired, and the employee behavior data acquisition model can be modified according to the actual running condition of software, so that the integrity of the acquired employee behavior data can be ensured;
2. the collected employee behavior data are stored in the database, so that the subsequent analysis on the employee behavior can be facilitated; when the employee behavior analysis message is obtained, corresponding employee behavior data is obtained from the database according to the employee behavior analysis message, and the employee behavior analysis data is sent to a management end of a manager, so that the manager can be helped to analyze the behavior of the employee according to actual requirements, the performance contribution of the employee is assessed and evaluated more objectively and fairly, and the effect of big data on the human resource management service is realized.
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FIG. 1 is a flow chart of employee tagging according to the present invention;
FIG. 2 is a ROC graph in an embodiment of the present invention;
FIG. 3 is a graph of learning ability dimension correlation analysis in an embodiment of the present invention;
fig. 4 is a schematic diagram of employee work behavior and capability index in the embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without inventive effort based on the embodiments of the present invention, are within the scope of the present invention.
Referring to fig. 1-4, the present invention provides a technical solution: an analysis method for employee behavior and ability dimensions is characterized in that: the method comprises the following steps:
s1: building an employee behavior data acquisition model, and acquiring employee behavior data according to the employee behavior data acquisition model;
s2: storing employee behavior data into a database;
s3: if the employee behavior analysis message is obtained, obtaining employee data to be obtained from employee behavior analysis;
s4: and acquiring employee behavior data from the database according to the employee data to be acquired, and sending the employee behavior data to the management terminal.
Further, step S1 includes:
s11: acquiring data acquisition latitude data, and setting an employee behavior data acquisition model according to the data acquisition latitude data;
s12: and acquiring latitude data by using data in the employee behavior data acquisition model to acquire corresponding employee behavior data.
Further, step S3 includes:
s31: acquiring a data acquisition type from the employee behavior analysis message;
s32: and acquiring the employee data to be acquired according to the data acquisition type.
Further, step S4 includes:
s41: classifying the employee behavior data according to the employee behavior analysis message to obtain data to be analyzed;
s42: and sending the data to be analyzed to a management end.
Further, step S1 further includes a method for establishing an employee group tag library:
s101: establishing personal portrait and organization portrait of employees through data acquisition;
s102: and (3) building an employee grouping label library through employee personal portrait and organization portrait: the formula is set up as follows:
the formula I is as follows:
Figure BDA0003396036820000061
the formula II is as follows:
Figure BDA0003396036820000062
the formula III is as follows:
Figure BDA0003396036820000063
in the formula: x represents a data point in the anatomical representation; μ represents a centroid in the tissue representation; n represents the number of employee portraits; i represents each feature constituting x;
s103: the contour coefficient S is defined by the tissue image, which is formulated as follows:
the formula four is as follows:
Figure BDA0003396036820000064
in the formula: a represents the similarity between the employee portrait data point and other data points in the organization portrait of the employee, and is equal to the average distance between the employee portrait data point and all other employee portrait data points in the same organization portrait; b represents the similarity of the employee representation data points to employee representation data points in other organizational representations equal to the average distance between the sample and all points in the next closest organizational representation;
and defining the value range of the contour coefficient as (-1, 1) according to the formula IV.
Further, the method also comprises an employee evaluation model establishing method, and comprises the following steps: establishing an employee evaluation model based on the organization sketch, wherein the formula is as follows:
the formula five is as follows:
Figure BDA0003396036820000071
in the formula, T (x; theta)m) Representing a decision tree composed of the organization images; thetamParameters of the decision tree; m is the tree formed by the employee portrait; .
The lifting tree algorithm adopts a forward distribution algorithm; determining an initial lifting tree f0(x) The model at step m is 0:
formula six: f. ofm(x)＝fm-1(x)+T(x；Θm)；
In the formula (f)m-1(x) For the current model, the parameters Θ of the next decision tree are determined by empirical risk minimizationm：
The formula seven:
Figure BDA0003396036820000072
and substituting the last prediction result into the gradient to obtain the training data of the round, and completing the establishment of the employee evaluation model.
An analysis system for employee behavior and competency dimensions, comprising:
the model acquisition module is used for setting an employee behavior data acquisition model and acquiring employee behavior data according to the employee behavior data acquisition model;
the storage module is used for storing the employee behavior data into a database;
the data acquisition module is used for acquiring employee data to be acquired from employee behavior analysis if the employee behavior analysis message is acquired;
and the data sending module is used for obtaining the employee behavior data from the database according to the employee data to be obtained and sending the employee behavior data to the management terminal.
Further, the model acquisition module comprises:
the latitude setting submodule is used for acquiring data acquisition latitude data and setting an employee behavior data acquisition model according to the data acquisition latitude data;
and the model acquisition submodule is used for acquiring latitude data by using the data in the employee behavior data acquisition model and acquiring corresponding employee behavior data.
This example proposes an implementation of the present invention, which is specifically as follows:
collecting field information of 149 factors in two aspects of working behaviors and abilities of the staff, wherein the working behaviors of the staff comprise 6 dimensions of labor discipline, safety behavior, wind control behavior, scientific research behavior, cultural and political behavior; employee competencies include learning ability, business ability, expression ability, collaboration ability, scientific innovation ability, cultural and cultural ability, and political diathesis 7 dimensions. And designing a factor assignment rule and a calculation rule in an expert library mode. Such as: the staff applies for the establishment of scientific research, reflects the initiative of staff's scientific research behavior to a certain extent, abstracts the staff as ' establishment of scientific research ' index, and designs the calculation rule as follows: the scientific standpoints ═ Σ (rank assignment × number) were assigned a value of 1.5, 1.2, 1, respectively. Finally, x indexes are combed out, as shown in figure 4,
nearly three years of workers such as labor models, advanced workers, innovation benchmarks and the like are classified as advanced worker samples (accounting for about 6% of the total number of statistical samples), and Pearson correlation analysis is performed on x preliminarily selected indexes one by one, as shown in FIG. 2 (taking the learning capacity dimension as an example). Selecting 36 core indexes with the correlation larger than x, and constructing a set of factor set for measuring the working behavior and the capability of the staff;
and based on the factor set of the working behaviors and abilities of the employees, dividing the index grades according to a reasonable threshold value according to the statistical analysis result of the core indexes. For example, the data for newsfeed numbers that affect expressiveness exhibits an extreme right bias in distribution, i.e., a median of 1.14, a mode of 0, a 75% quantile of 6.07, a 95% quantile of greater than 25, and a maximum of 163.56. The statistical result shows that the number of news releases of most employees is 0, and the number of news releases of few employees is greater than the median. The scoring criteria for this field are divided into three levels by analysis, with employee samples less than the median scored as "0", employee samples between the median and 75% quantile scored as "1", and employee samples greater than 75% quantile scored as "2". The significance of the numerical value of the factor score corresponding to the actual service evaluation is as follows: the '2' is an advanced sample, the staff actively submits, pays attention to the current affairs and has thinking and analysis on the expansion of the business, the behavior ability of the staff is visually and effectively observed through establishing the dimensionality, the management staff analyzes the behavior of the staff according to the actual requirement, the performance contribution of the staff is more objectively and fairly assessed, and the enabling of big data to the human resource management business is realized.
The foregoing is illustrative of the preferred embodiments of this invention, and it is to be understood that the invention is not limited to the precise form disclosed herein and that various other combinations, modifications, and environments may be resorted to, falling within the scope of the concept as disclosed herein, either as described above or as apparent to those skilled in the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (8)

1. An analysis method for employee behavior and ability dimensions is characterized in that: the method comprises the following steps:
s1: building a staff behavior data acquisition model, and acquiring staff behavior data according to the staff behavior data acquisition model;
s2: storing the employee behavior data into a database;
s3: if the employee behavior analysis message is acquired, acquiring employee data to be acquired from the employee behavior analysis;
s4: and acquiring the employee behavior data from the database according to the employee data to be acquired, and sending the employee behavior data to a management terminal.
2. The method for analyzing employee behavior and competence dimensions of claim 1, wherein step S1 comprises:
s11: acquiring data acquisition latitude data, and setting the employee behavior data acquisition model according to the data acquisition latitude data;
s12: and acquiring corresponding employee behavior data by using the data acquisition latitude data in the employee behavior data acquisition model.
3. The method for analyzing employee behavior and competence dimensions of claim 1, wherein step S3 comprises:
s31: acquiring a data acquisition type from the employee behavior analysis message;
s32: and acquiring the employee data to be acquired according to the data acquisition type.
4. The method for analyzing employee behavior and competence dimensions of claim 1, wherein: step S4 includes:
s41: classifying the employee behavior data according to the employee behavior analysis message to obtain data to be analyzed;
s42: and sending the data to be analyzed to the management terminal.
5. The method for analyzing employee behavior and competence dimensions of claim 1, wherein: the step S1 further includes a method for establishing an employee group tag library:
s101: establishing personal portrait and organization portrait of employees through data acquisition;
s102: and (3) building an employee grouping label library through employee personal portrait and organization portrait: the formula is set up as follows:
the formula I is as follows:
Figure FDA0003396036810000021
the formula II is as follows:
Figure FDA0003396036810000022
the formula III is as follows:
Figure FDA0003396036810000023
in the formula: x represents a data point in the anatomical representation; μ represents a centroid in the tissue representation; n represents the number of employee portraits; i represents each feature constituting x;
s103: the contour coefficient S is defined by the tissue image, which is formulated as follows:
the formula four is as follows:
Figure FDA0003396036810000024
in the formula: a represents the similarity between the employee portrait data point and other data points in the organization portrait of the employee, and is equal to the average distance between the employee portrait data point and all other employee portrait data points in the same organization portrait; b represents the similarity of the employee representation data points to employee representation data points in other organizational representations equal to the average distance between the sample and all points in the next closest organizational representation;
and defining the value range of the contour coefficient as (-1, 1) according to the formula IV.
6. The method for analyzing employee behavior and competence dimensions of claim 5, wherein: the employee evaluation model building method comprises the following steps: establishing an employee evaluation model based on the organization sketch, wherein the formula is as follows:
the formula five is as follows:
Figure FDA0003396036810000031
in the formula, T (x; theta)m) Representing a decision tree composed of the organization images; thetamParameters of the decision tree; m is the tree formed by the employee portrait;
tree calculation promotionThe method adopts a forward distribution algorithm; determining an initial lifting tree f0(x) The model at step m is 0:
formula six: f. ofm(x)＝fm-1(x)+T(x；Θm)；
In the formula (f)m-1(x) For the current model, the parameters Θ of the next decision tree are determined by empirical risk minimizationm：
The formula seven:
Figure FDA0003396036810000032
and substituting the last prediction result into the gradient to obtain the training data of the round, and completing the establishment of the employee evaluation model.
7. An analysis system for staff behavior and ability dimensionality is characterized in that: the method comprises the following steps:
the model acquisition module is used for setting an employee behavior data acquisition model and acquiring employee behavior data according to the employee behavior data acquisition model;
the storage module is used for storing the employee behavior data into a database;
the data acquisition module is used for acquiring employee data to be acquired from employee behavior analysis if the employee behavior analysis message is acquired;
and the data sending module is used for obtaining the employee behavior data from the database according to the employee data to be obtained and sending the employee behavior data to a management terminal.
8. The system for analyzing employee behavior and competence dimensions of claim 7, wherein the model collection module comprises:
the latitude setting submodule is used for acquiring data acquisition latitude data and setting the employee behavior data acquisition model according to the data acquisition latitude data;
and the model acquisition submodule is used for acquiring latitude data by using the data in the employee behavior data acquisition model to acquire corresponding employee behavior data.
CN202111511854.2A 2021-12-07 2021-12-07 Analysis method for employee behavior and ability dimension Pending CN114399158A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117973949A (en) * 2024-04-01 2024-05-03 深圳市领存技术有限公司 Evaluation generation method, device, equipment and storage medium based on large model

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117973949A (en) * 2024-04-01 2024-05-03 深圳市领存技术有限公司 Evaluation generation method, device, equipment and storage medium based on large model

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