CN115689316A - Employee efficiency evaluation method, system and medium based on project completion condition - Google Patents
Employee efficiency evaluation method, system and medium based on project completion condition Download PDFInfo
- Publication number
- CN115689316A CN115689316A CN202110830907.0A CN202110830907A CN115689316A CN 115689316 A CN115689316 A CN 115689316A CN 202110830907 A CN202110830907 A CN 202110830907A CN 115689316 A CN115689316 A CN 115689316A
- Authority
- CN
- China
- Prior art keywords
- project
- model
- preset
- determining
- evaluated
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The application discloses a method, a system and a medium for evaluating the efficiency of employees based on project completion conditions, which solve the technical problem that the work efficiency of the employees cannot be evaluated finely by utilizing project data of project completion of the employees in the existing enterprise management. The method comprises the following steps: determining a project knowledge graph based on project data of a project completed by an employee to be evaluated; acquiring historical project data, and training a preset model based on the historical project data to determine a scoring model; the system comprises a preset model, a scoring model and a control module, wherein the preset model comprises a first preset model and a second preset model, and the scoring model comprises a single-module scoring model and a comprehensive scoring model; the single module scoring model is a model for determining the score obtained by a certain module in the staff completion project to be evaluated; and determining the comprehensive score of the project completed by the staff to be evaluated based on the project knowledge graph and the scoring model. According to the method, the staff working efficiency is evaluated finely.
Description
Technical Field
The application relates to the technical field of enterprise management, in particular to a method, a system and a medium for evaluating staff efficiency based on project completion conditions.
Background
In the process of enterprise development, along with the continuous expansion of enterprise scale, the development mode of the enterprise needs to be gradually converted into fine management from the rough expansion type development. For enterprise management layers and human resource departments, how to accurately and effectively evaluate the working efficiency of the staff has great significance for the fine management of enterprises, and the system can play a great role in performance assessment and position assessment of the staff.
The project completion condition is an important mode for examining the energy efficiency of the staff, but because different projects have different difficulties and different completion qualities, the roles of the staff in the projects are different, and the work efficiency of the staff is difficult to be directly examined by utilizing the project completion condition through a simple statistical mode.
Disclosure of Invention
The embodiment of the application provides a method, a system and a medium for evaluating the efficiency of employees based on project completion conditions, and solves the technical problem that the work efficiency of the employees cannot be evaluated finely by utilizing project data of project completion of the employees in the existing enterprise management.
In a first aspect, an embodiment of the present application provides an employee efficiency evaluation method based on project completion conditions, including: determining a project knowledge graph based on project data of a project completed by an employee to be evaluated; acquiring historical project data, and training a preset model based on the historical project data to determine a scoring model; the system comprises a preset model, a scoring model and a control model, wherein the preset model comprises a first preset model and a second preset model, and the scoring model comprises a single-module scoring model and a comprehensive scoring model; the single module scoring model is a model for determining the score obtained by a certain module in the staff completion project to be evaluated; and determining the comprehensive score of the project completed by the staff to be evaluated based on the project knowledge graph and the scoring model.
According to the embodiment of the application, the project and the staff are used as nodes, the knowledge graph formed by the project, the staff to be evaluated and the incidence relation is constructed, and various label data are contained in the knowledge graph, so that the situation that the staff to be evaluated completes the project is clearly shown. The preset model is trained by utilizing a large amount of historical project data, so that the scoring model is obtained, and an accurate evaluation result can be obtained when the scoring model evaluates the staff to be evaluated. According to the method, the staff to be evaluated can evaluate the finished project, so that the non-standardization of the traditional investigation mode evaluation is avoided, and a large amount of time cost and labor cost are saved.
In an implementation manner of the present application, determining a project knowledge graph based on project data of a project completed by an employee to be evaluated specifically includes: determining a structure of a knowledge graph based on project historical data; determining a preset label of a knowledge graph based on the specific type of the project and the project role of the employee to be evaluated; the preset labels comprise preset project labels, preset employee labels to be evaluated and preset association relation labels; determining the label attribute of a preset label based on the historical data of the project; and integrating the structure of the knowledge graph, the preset labels and the label attributes corresponding to the preset labels to determine the project knowledge graph.
In an implementation manner of the present application, after determining the preset tag based on the specific type of the project and the project role of the employee to be evaluated, the method further includes: determining each single-module knowledge graph in the completion project of the employee to be evaluated based on the project knowledge graph; determining a scoring tag in the preset tags based on the type of the preset tags; the scoring labels comprise positive labels, negative labels and correction labels; the positive label is a label which increases the score of the staff to be evaluated, the negative label is a label which reduces the score of the staff to be evaluated, and the correction label is a label which corrects the unreasonable score of the staff completion project.
In one implementation manner of the present application, before obtaining the historical item data, the method further includes: and creating a historical item index database based on a preset item server to store the historical item data.
In an implementation manner of the present application, training a preset model based on historical project data to determine a scoring model specifically includes: acquiring historical item data stored in a historical item index database, and processing the historical item data to form an item data training set; training a preset model based on the project data training set to determine a scoring model; the historical project data comprises relevant records of project completion of the employees, and single-module scores and comprehensive scores of project completion of the corresponding employees.
In an implementation manner of the application, determining a comprehensive score of a project completion condition of an employee to be evaluated based on a knowledge graph and a scoring model of the project completion condition of the employee to be evaluated specifically includes: determining a scoring label and a weight coefficient of each single module based on the project knowledge graph; determining the single module score of the staff to be evaluated in each single module based on the single module score model, the score label attribute and the weight coefficient of the score label; and determining the comprehensive score of the project completed by the staff to be evaluated based on the comprehensive score model, the single module score and the weight coefficient of each single module.
In one implementation manner of the present application, the first predetermined model is
Wherein n is P Is the number of forward tags in the industry, n N Is the number of negative tags in the industry, n C The number of labels is revised for that industry. P wei Value of i forward label of employee w in single module e, a i For employee w in a single module eWeighting factor, N, of i forward labels wei Value b for the ith negative label of employee w in single module e i Weight coefficient for i-th negative label of employee w in single module e, C wi Value of the ith correction label of employee w, c P ，c N ，a 0 ，b 0 And are model parameters.
In one implementation manner of the present application, the second predetermined model is
Wherein S is wi For the single module score obtained by employee w in single module i, D i Is the weighting coefficient of the single module i, and k is the nonlinear exponent.
In a second aspect, an embodiment of the present application further provides an employee efficiency evaluation system based on project completion conditions, including: the system comprises a knowledge graph determining module, a grading model determining module and a grading determining module; the knowledge graph determining module is used for determining a project knowledge graph based on project data of a project completed by an employee to be evaluated; the scoring model determining module is used for acquiring historical project data and training a preset model based on the historical project data to determine a scoring model; the system comprises a preset model, a scoring model and a control model, wherein the preset model comprises a first preset model and a second preset model, and the scoring model comprises a single-module scoring model and a comprehensive scoring model; the single-module scoring model is a model for determining the score obtained by a certain module in the completion project of the employee to be evaluated; and the score determining module is used for determining the comprehensive score of the project completed by the staff to be evaluated based on the project knowledge graph and the score model.
In a third aspect, an embodiment of the present application further provides a nonvolatile computer storage medium for employee performance evaluation based on project completion conditions, where computer-executable instructions are stored, and the computer-executable instructions are configured to: determining a project knowledge graph based on project data of a project completed by an employee to be evaluated; acquiring historical project data, and training a preset model based on the historical project data to determine a scoring model; the system comprises a preset model, a scoring model and a control module, wherein the preset model comprises a first preset model and a second preset model, and the scoring model comprises a single-module scoring model and a comprehensive scoring model; the single module scoring model is a model for determining the score obtained by a certain module in the staff completion project to be evaluated; and determining the comprehensive score of the project completed by the staff to be evaluated based on the project knowledge graph and the scoring model.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a flowchart of an employee performance evaluation method based on project completion according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of an employee efficiency evaluation system based on project completion according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only a few embodiments of the present application, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In order to comb the complex relation between the project and the staff and facilitate further design of an algorithm to calculate the staff energy efficiency, a knowledge graph can be used as a tool. The concept of knowledge graph was born in 2012 and was first proposed by Google corporation. Knowledge maps were proposed to accurately describe the relationships between people, things, and were first applied to search engines. The knowledge graph is a knowledge database for describing text semantics and establishing entity relations in nature. In general, we can use a relational graph to represent a knowledge graph. The knowledge graph comprises three components: entities, relationships, and attributes. The elements that make up the knowledge-graph include entities, relationships, and attributes. An entity refers to things that exist objectively and can be distinguished from each other, and may be a specific person, thing, or an abstract concept or connection. An entity is the most basic element in a knowledge-graph. In a knowledge graph, edges represent relationships in the knowledge graph that are used to represent some kind of connection between different entities. In addition, the entities and relationships in the knowledge-graph may have respective attributes.
The knowledge graph is used for facilitating the cleaning of the relation between the project and the staff, and the knowledge graph formed by the project, the staff and the association relation is constructed by taking the project and the staff as nodes. On the basis of the basic structure of the knowledge graph, relevant attributes are set for each relation and each node to form a knowledge graph of project completion conditions, and therefore algorithms can be further designed to evaluate the efficiency of the staff by utilizing the relevant relations and the attributes on the knowledge graph.
The embodiment of the application provides a method, a system and a medium for evaluating the efficiency of employees based on project completion conditions, and solves the technical problem that the work efficiency of the employees cannot be evaluated finely by utilizing project data of project completion of the employees in the existing enterprise management.
The technical solutions proposed in the embodiments of the present application are described in detail below with reference to the accompanying drawings.
Fig. 1 is a flowchart of an employee performance evaluation method based on project completion provided in an embodiment of the present application. As shown in fig. 1, the employee efficiency evaluation method based on project completion provided in the embodiment of the present application specifically includes the following steps:
Because the elements forming the knowledge graph comprise entities, association relations and attributes, in the embodiment of the application, the entities are projects completed by the staff to be evaluated and the staff to be evaluated, and the association relations are events completed by roles played by the staff to be evaluated in the process of completing the projects. It should be noted that, when the employee to be evaluated completes the project, the employee may include more than or equal to two role identities, and therefore, the association relationship cannot be made for a single role identity. Taking IT industry software development as an example, in developers, there may exist employees who serve as project supervisor identities and business personnel identities communicating with target customers, and therefore, the association relationship cannot be reflected only in project completion.
After determining the entities and the association relationship of the knowledge graph, the structural basis of the project knowledge graph is determined, so that the complete knowledge graph is determined, and the attribute of the knowledge graph is required to be determined. Determining the attributes of the knowledge graph, namely determining preset labels of the knowledge graph based on the specific types of the projects and the project roles of the employees to be evaluated, wherein the preset labels can display the composition of the attributes of the knowledge graph. It can be understood that the preset tags can be divided into preset project tags, preset employee tags to be evaluated and preset association relationship tags. For example, the preset project tags may include project difficulty level, estimated construction period, actual construction period, function implementation degree, etc., the preset staff tags to be evaluated may include role names, etc., and the preset association relationship tags include work content, workload proportion, completion construction period, etc.
After the preset labels of the knowledge graph are determined, assignment is carried out on the preset labels based on project historical data, and therefore the label attributes of the preset labels are determined. After the structure of the knowledge graph, the preset tags of the knowledge graph and the tag attributes corresponding to the preset tags are determined, all elements forming the project knowledge graph are obtained, and the structure of the knowledge graph, the preset tags and the tag attributes corresponding to the preset tags are integrated, so that the project knowledge graph is determined.
And 102, acquiring historical project data, and training a preset model based on the historical project data to determine a grading model.
In one embodiment of the present application, before obtaining the historical item data to train the preset model, a historical item index database should be created based on a preset item server to store the historical item data. It should be noted that, on the preset project server, in addition to creating the historical project index database, a complete service should be deployed to implement data update of the historical project index database and training of the preset model.
IT should be noted that the historical item data stored in the historical item index database does not need to have similarity, and taking IT industry software development companies as an example, the historical item index database may store developed software item data, bid item data, annual meeting item data, and the like.
When the historical project data is obtained to train the preset model, the historical project data similar to the project type of the project completed by the employee to be evaluated in the historical project index database is obtained based on the project type of the project completed by the employee to be evaluated. It should be noted that the historical item data stored in the historical item index database should include relevant records of employee completion items, and single module scores and comprehensive scores of corresponding employee completion items; wherein, the single module score is the score obtained by a certain module in the completion project of the staff to be evaluated.
After the historical project data are obtained, a project data training set is formed by a data set obtained by sorting the historical project data, and the project data training set is sequentially input into a preset model for training until a convergent scoring model is output.
It should be noted that the preset model should be able to realize single module scoring and comprehensive scoring of the employee in the project. Each item in the project data training set should be presented in the form of a knowledge-graph, and the knowledge-graph of the item includes the employee's individual module knowledge-graphs. In addition, the scoring labels in the preset labels and the weight coefficients of the scoring labels and the single modules are determined based on the types of the preset labels in the knowledge graph. The scoring tags should include positive tags, negative tags, and correction tags; the positive label is a label which increases the score of the staff to be evaluated, the negative label is a label which reduces the score of the staff to be evaluated, and the correction label is a label which corrects the unreasonable score of the staff completion project.
In one embodiment of the present application, a rational preset model is provided, comprising a first preset model and a second preset model. The first preset model is used for training to obtain a single module scoring model, and the second prediction model is used for training to obtain a comprehensive scoring model.
The first preset model is
Wherein n is P Is the number of forward tags in the industry, n N Is the number of negative tags in the industry, n C The number of tags is modified for the industry. P wei Value of the ith forward label of employee w in single module e, a i Weight coefficient for the ith forward label of employee w in single module e, N wei Value b for the ith negative label of employee w in single module e i Weight coefficient for i-th negative label of employee w in single module e, C wi Value of the ith correction label of employee w, c P ，c N ，a 0 ，b 0 And are model parameters.
In order to ensure that the score of a single module of the employee cannot be infinitely increased or infinitely decreased no matter whether the tag is a positive tag or a negative tag, thereby avoiding the influence of the single module on the comprehensive score, the score of the single item should have a theoretical maximum value and a theoretical minimum value, and therefore, the parameter c is set in the first preset model of the embodiment of the application P And c N So that the first preset model can ensure the single module scoring S we The value range of N And c P In the meantime.
The second predetermined pattern is
Wherein S is wi For the single module score obtained by employee w in single module i, D i Is the weighting coefficient of the single module i, and k is the nonlinear exponent.
The comprehensive score of the employee to be evaluated is obtained by comprehensively calculating the single module score obtained in each single module, but cannot be directly inputLine accumulation, each item should have a corresponding weight coefficient D according to its duration or importance i ,＝1,…, D And carrying out nonlinear weighted average on the comprehensive score based on the weight coefficient to obtain the comprehensive score. Wherein the calculation S can be adjusted by adjusting the size of k w Degree of non-linearity in the process, when k<1, the accumulation of the workload has the amplification effect on the score, when k is<1, accumulation of workload has a diminishing effect on the score.
It can be understood that the project data training set is input into the preset model for training, so as to obtain the model parameters of the preset model.
In an embodiment of the application, in addition to training the model parameters by using a method based on machine learning training, the parameters may be defined by using managers of companies or human resource departments and related experts in the business field, and the model parameters in the model are artificially defined according to factors such as the importance degree of different labels, the importance and difficulty of the employees to be evaluated in undertaking the role responsible work, and the like.
And 103, determining the comprehensive score of the project completed by the staff to be evaluated based on the project knowledge graph and the scoring model.
Before inputting a project knowledge graph of a project completed by an employee to be evaluated into a scoring model for calculation, determining each single-module knowledge graph in the project completed by the employee to be evaluated based on the project knowledge graph; and determining a scoring label in the preset labels and determining the scoring label and the weight coefficient of each single module based on the type of the preset label in the project knowledge graph. And then, inputting the scoring label attributes and the weighting coefficients of the scoring labels in the knowledge graph of each single module into a trained single module scoring model, and determining the single module scoring of the staff to be evaluated in each single module.
And after determining the single-module score of the employee to be evaluated in each single module, determining the comprehensive score of the employee to be evaluated for completing the project based on the comprehensive scoring model, the single-module score and the weight coefficient of each single module.
According to the embodiment of the application, the relation between the staff to be evaluated and the project shown by the knowledge graph is utilized to derive various label characteristics and attributes based on the graph; calculating a model through a preset relatively reasonable score; determining relevant parameters of the model by using a historical data or expert knowledge method, and ensuring the accuracy of the model; and finally, carrying out nonlinear weighted calculation on the single module score of each single module of the staff to be evaluated to obtain the comprehensive score of the staff to be evaluated. The preset model used by the evaluation method has both reasonability and flexibility, and different parameters and label definition modes can be provided for the scoring modes of different companies and different fields.
Based on the same inventive concept, the embodiment of the application further provides an employee efficiency evaluation system based on project completion conditions, and the structural schematic diagram of the system is shown in fig. 2.
Fig. 2 is a schematic structural diagram of an employee performance evaluation system based on project completion provided in an embodiment of the present application. As shown in fig. 2, an employee performance evaluation system 200 based on project completion according to an embodiment of the present application includes: a knowledge graph determining module 201, a scoring model determining module 202 and a scoring determining module 203.
Those skilled in the art will appreciate that the configuration of the employee performance assessment system based on project completion shown in FIG. 2 is not intended to be limiting of the employee performance assessment system based on project completion, and in fact, the employee performance assessment system based on project completion may include more or less components than shown in FIG. 2, or some components may be combined, or an arrangement of different components may be used.
In an embodiment of the present application, the knowledge graph determining module 201 is configured to determine a project knowledge graph based on project data of a project completed by an employee to be evaluated; the scoring model determining module 202 is configured to obtain historical project data, and train a preset model based on the historical project data to determine a scoring model; the system comprises a preset model, a scoring model and a control module, wherein the preset model comprises a first preset model and a second preset model, and the scoring model comprises a single-module scoring model and a comprehensive scoring model; the single module scoring model is a model for determining the score obtained by a certain module in the staff completion project to be evaluated; and the score determining module 203 is used for determining the comprehensive score of the project completed by the staff to be evaluated based on the project knowledge graph and the score model.
Some embodiments of the present application provide a non-volatile computer storage medium corresponding to the employee performance evaluation based on project completion of fig. 1, having stored thereon computer-executable instructions configured to:
determining a project knowledge graph based on project data of a project completed by an employee to be evaluated;
acquiring historical project data, and training a preset model based on the historical project data to determine a scoring model; the system comprises a preset model, a scoring model and a control model, wherein the preset model comprises a first preset model and a second preset model, and the scoring model comprises a single-module scoring model and a comprehensive scoring model; the single module scoring model is a model for determining the score obtained by a certain module in the staff completion project to be evaluated;
and determining the comprehensive score of the project completed by the staff to be evaluated based on the project knowledge graph and the scoring model.
The embodiments in the present application are described in a progressive manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. Especially, for the internet of things device and medium embodiments, since they are substantially similar to the method embodiments, the description is simple, and the relevant points can be referred to the partial description of the method embodiments.
The system and the medium provided by the embodiment of the application correspond to the method one by one, so the system and the medium also have the similar beneficial technical effects as the corresponding method, and the beneficial technical effects of the method are explained in detail above, so the beneficial technical effects of the system and the medium are not described again.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both permanent and non-permanent, removable and non-removable media, may implement the information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising a … …" does not exclude the presence of another identical element in a process, method, article, or apparatus that comprises the element.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art to which the present application pertains. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. An employee efficiency evaluation method based on project completion conditions is characterized by comprising the following steps:
determining a project knowledge graph based on project data of a project completed by an employee to be evaluated;
acquiring historical project data, and training a preset model based on the historical project data to determine a scoring model; the preset model comprises a first preset model and a second preset model, and the scoring model comprises a single-module scoring model and a comprehensive scoring model; the single module scoring model is a model for determining the score obtained by a certain module in the staff completion project to be evaluated;
and determining the comprehensive score of the project completed by the staff to be evaluated based on the project knowledge graph and the scoring model.
2. The employee performance evaluation method based on project completion according to claim 1, wherein determining a project knowledge graph based on project data of project completion of employees to be evaluated specifically comprises:
determining a structure of the knowledge-graph based on the project history data;
determining a preset label of the knowledge graph based on the specific type of the project and the project role of the employee to be evaluated; the preset labels comprise preset project labels, preset staff labels to be evaluated and preset association relation labels;
determining the label attribute of the preset label based on the project historical data;
and integrating the structure of the knowledge graph, the preset labels and the label attributes corresponding to the preset labels to determine the project knowledge graph.
3. The employee performance evaluation method based on project completion of claim 2, wherein after determining the preset label based on the specific type of the project and the project role of the employee to be evaluated, the method further comprises:
determining each single-module knowledge graph in the completion project of the employee to be evaluated based on the project knowledge graph;
determining a scoring label in the preset labels based on the type of the preset labels;
wherein the scoring labels comprise positive labels, negative labels and correction labels; the positive label is a label which plays a role in adding scores to the scores of the employees to be evaluated, the negative label is a label which plays a role in subtracting the scores of the employees to be evaluated, and the correction label is a label which plays a role in correcting the unreasonable scores of the employees to finish the projects.
4. The employee performance assessment method based on project completion of claim 1, wherein prior to obtaining historical project data, the method further comprises:
and creating a historical item index database based on a preset item server to store the historical item data.
5. The employee performance evaluation method based on project completion according to claim 4, wherein training a preset model based on the historical project data to determine a scoring model specifically comprises:
acquiring the historical item data stored in the historical item index database, and processing the historical item data to form an item data training set;
training the preset model based on the project data training set to determine a scoring model;
the historical project data comprises relevant records of project completion of the employees, and single-module scores and comprehensive scores of project completion of the corresponding employees.
6. The employee efficiency evaluation method based on project completion of claim 3, wherein determining the comprehensive score of the employee completion project to be evaluated based on the employee project completion knowledge graph to be evaluated and the scoring model specifically comprises:
determining the scoring labels and the weight coefficients of the single modules based on the project knowledge graph;
determining the single module score of the staff to be evaluated in each single module based on the single module score model, the score label attribute and the weight coefficient of the score label;
and determining the comprehensive score of the project completed by the staff to be evaluated based on the comprehensive score model, the single module score and the weight coefficient of each single module.
7. The employee performance evaluation method based on project completion status according to claim 4, wherein the first predetermined model is
Wherein n is P Is the number of forward tags in the industry, n N Is the number of negative tags in the industry, n C The number of tags is modified for the industry. P wei Value of i forward label of employee w in single module e, a i Weight coefficient for the ith forward label of employee w in single module e, N wei Value for the ith negative label of employee w in single module e, b i Weight coefficient for i-th negative label of employee w in single module e, C wi Value of the ith correction label of employee w, c P ，C N ，a 0 ，b 0 And are model parameters.
8. The employee performance evaluation method based on project completion status according to claim 4, wherein the second predetermined model is
Wherein S is wi Sheets obtained in sheet Module i for employee wModule rating, D i Is the weighting coefficient of the single module i, and k is the nonlinear exponent.
9. An employee performance evaluation system based on project completion, the system comprising: the system comprises a knowledge graph determining module, a grading model determining module and a grading determining module;
the knowledge graph determining module is used for determining a project knowledge graph based on project data of a project completed by an employee to be evaluated;
the scoring model determining module is used for acquiring historical project data and training a preset model based on the historical project data to determine a scoring model; the preset models comprise a first preset model and a second preset model, and the scoring model comprises a single-module scoring model and a comprehensive scoring model; the single module scoring model is a model for determining the score obtained by a certain module in the staff completion project to be evaluated;
and the score determining module is used for determining the comprehensive score of the project completed by the staff to be evaluated based on the project knowledge graph and the score model.
10. A non-transitory computer storage medium for employee performance assessment based on project completion, the computer storage medium having stored thereon computer-executable instructions configured to:
determining a project knowledge graph based on project data of a project completed by an employee to be evaluated;
acquiring historical project data, and training a preset model based on the historical project data to determine a scoring model; the preset model comprises a first preset model and a second preset model, and the scoring model comprises a single-module scoring model and a comprehensive scoring model; the single module scoring model is a model for determining the score obtained by a certain module in the staff completion project to be evaluated;
and determining the comprehensive score of the project completed by the staff to be evaluated based on the project knowledge graph and the scoring model.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110830907.0A CN115689316A (en) | 2021-07-22 | 2021-07-22 | Employee efficiency evaluation method, system and medium based on project completion condition |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110830907.0A CN115689316A (en) | 2021-07-22 | 2021-07-22 | Employee efficiency evaluation method, system and medium based on project completion condition |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115689316A true CN115689316A (en) | 2023-02-03 |
Family
ID=85044739
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110830907.0A Pending CN115689316A (en) | 2021-07-22 | 2021-07-22 | Employee efficiency evaluation method, system and medium based on project completion condition |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115689316A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117057683A (en) * | 2023-10-13 | 2023-11-14 | 四川中电启明星信息技术有限公司 | Staff portrait management system based on knowledge graph and multi-source application data |
-
2021
- 2021-07-22 CN CN202110830907.0A patent/CN115689316A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117057683A (en) * | 2023-10-13 | 2023-11-14 | 四川中电启明星信息技术有限公司 | Staff portrait management system based on knowledge graph and multi-source application data |
| CN117057683B (en) * | 2023-10-13 | 2023-12-22 | 四川中电启明星信息技术有限公司 | Staff portrait management system based on knowledge graph and multi-source application data |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Wibowo et al. | Supply chain management strategy for recycled materials to support sustainable construction | |
| Yamova et al. | Integral valuation of an enterprise's competitiveness in the industrial economy | |
| Afsharian et al. | Generalized DEA: an approach for supporting input/output factor determination in DEA | |
| Sándor et al. | A measuring tool for the digital maturity of small and medium-sized enterprises | |
| Oshodi et al. | Comparing univariate techniques for tender price index forecasting: Box-Jenkins and neural network model | |
| Teslenko et al. | 3-Level approach to the projects planning | |
| Agrawal | GDP modelling and forecasting using ARIMA: an empirical study from India | |
| CN115689316A (en) | Employee efficiency evaluation method, system and medium based on project completion condition | |
| Diyanty et al. | Intellectual Capital and Firm Performance: Applying a Modified Value-Added Coefficient | |
| Kang et al. | Short-term forecast model of apartment Jeonse prices using search frequencies of news article keywords | |
| CN107220778A (en) | A kind of method, device and the electronic equipment of employee's credit appraisal and application | |
| Kapur et al. | Multi-generational innovation diffusion modelling: a two dimensional approach | |
| Gurbaxani | The demand for information technology capital An empirical analysis | |
| Pletnev et al. | Integral Evaluation of Business Success: Methodology and Case of Russian SME | |
| Jiang et al. | Identifying determinants of demand for construction using an econometric approach | |
| CN108985646A (en) | A kind of tracking for financial regulation cash flow in producing | |
| Relich | A knowledge management system for evaluating the potential of a new product | |
| Krylov | Applied strategic after-sale service analysis as a new instrument to research its strategic aspects | |
| Raphael et al. | Integrating financial and non-financial measures to measure the performance of commercial banks: Evidence from Tanzania | |
| Sachdeva et al. | Optimal launch of a new generation of technology: a multi attribute approach discrete time diffusion process | |
| Kozyk et al. | Econometric pricing model for R&D products in transfer agreements | |
| Krylov | Applied Strategic Operational Analysis as New Tool to Research Strategic Aspects of Organization Operational Activity | |
| Nigmatullin | On Improving the Model of Forecasting Innovative Regional Development | |
| Huang et al. | Input modelling for financial simulations using the bootstrap | |
| Islami et al. | Assessment of Motor Vehicle Repair Shop Service Quality Perception Using the SERVPERF Model and Lean Service |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |