Employee performance evaluation method, device, computer equipment and storage medium

Abstract

The embodiment of the present invention relates to an employee performance evaluation method, device, computer equipment and storage medium, including: obtaining employee work data of all employees in an enterprise; constructing a collaborator network model based on the employee work data; assigning an initial weight to each collaborator node in the collaborator network model based on a preset rule, and calculating the mutual influence weight between the collaborator nodes; calculating the collaborator project score of each collaborator node based on the employee work data and the collaborator network model; and evaluating the employee performance of each employee in the enterprise based on the mutual influence weight between the collaborator nodes and the collaborator project score. Thus, a comprehensive and objective employee performance evaluation tool can be provided for the enterprise, and it can also be used in aspects such as team building, project allocation, and talent management to help the enterprise better exert the potential of employees, thereby improving the overall competitiveness of the enterprise.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of complex network technology, and in particular to an employee performance evaluation method, device, computer equipment and storage medium. Background Art

[0002] In today's increasingly fierce workplace competition, with the development of internal social networks in enterprises, cooperation and interaction between employees have become increasingly frequent and complex. Therefore, how to fairly and impartially evaluate employee performance and help companies better tap the potential of employees in terms of team building, project allocation, talent management, etc. has become an important challenge facing many companies. Summary of the invention

[0003] In view of this, in order to solve the above technical problems or part of the technical problems, the embodiments of the present invention provide an employee performance evaluation method, apparatus, computer equipment and storage medium.

[0004] In a first aspect, an embodiment of the present invention provides an employee performance evaluation method, comprising:

[0005] Obtain employee work data of all employees in the enterprise;

[0006] Building a collaborator network model based on the employee work data;

[0007] Assign initial weights to each partner node in the partner network model based on preset rules, and calculate the mutual influence weights between partner nodes;

[0008] Calculating a collaborator project score of each collaborator node based on the employee work data and the collaborator network model;

[0009] The employee performance of each employee in the enterprise is evaluated based on the mutual influence weights between the partner nodes and the partner project scores.

[0010] In a possible implementation, the method further includes:

[0011] Obtain project data of all employees in the enterprise;

[0012] Employee cooperation data of all employees is determined based on the project data.

[0013] In a possible implementation, the method further includes:

[0014] Based on the employee cooperation data, a collaborator network model is constructed with employee information as collaborator nodes of the collaborator network model and projects jointly participated by employees as edges of the collaborator network model.

[0015] In a possible implementation, the method further includes:

[0016] Assign an initial weight to each collaborator node in the collaborator network model based on the number of projects each employee participates in;

[0017] The mutual influence weights between the partner nodes are calculated based on the initial weights by using the first formula and the second formula, where the first formula is:

[0018] Among them, W(A l ,A k ) is the influence weight of partner node l on partner node k, (A l ,A k ) is the number of projects that partner node l and partner node k jointly collaborate on, PA k is the number of projects in which the collaborator node k participates;

[0019] The second formula is:

[0020] Among them, W(A k ,A l ) is the influence weight of partner node k on partner node l, (A k ,A l ) is the number of projects that partner node l and partner node k jointly collaborate on, PA l is the number of projects in which partner node l participates.

[0021] In a possible implementation, the method further includes:

[0022] Determine project levels based on project data for each employee;

[0023] The partner project score of each partner node is calculated by a third formula based on the pre-divided customer level, the project level and the partner network model. The third formula is:

[0024] Among them, P is the project set, pub i For the i-th item, r(pub i ) is the item level, α is a parameter, and its value is (0<α<1).

[0025] In a possible implementation, the method further includes:

[0026] Based on the mutual influence weights between the collaborator nodes and the collaborator project scores, the PageRank algorithm is applied to calculate the final score of each employee through the fourth and fifth formulas. The fourth formula is:

[0027] Where, d is the damping factor, and its value is (0≤d≤1);

[0028] The fifth formula is:

[0029] Where n is the total number of partners, W(A i ,A j ) is the influence weight of partner node i on partner node j, λ(A i ) is the collaborator project score, X is the final score of each employee, W(Ak,Aj) is the influence weight of collaborator k on collaborator j, λ(A k ) is the final score of collaborator k;

[0030] Evaluate employee performance of each employee based on their final score.

[0031] In a possible implementation, the method further includes:

[0032] Employee management is performed based on said employee performance.

[0033] In a second aspect, an embodiment of the present invention provides an employee performance evaluation device, comprising:

[0034] The acquisition module is used to obtain the employee work data of all employees in the enterprise;

[0035] A construction module, used for constructing a collaborator network model based on the employee work data;

[0036] A calculation module, used to assign an initial weight to each partner node in the partner network model based on a preset rule, and calculate the mutual influence weights between the partner nodes;

[0037] A calculation module, used for calculating a collaborator project score of each collaborator node based on the employee work data and the collaborator network model;

[0038] An evaluation module is used to evaluate the employee performance of each employee in the enterprise based on the mutual influence weights between the partner nodes and the partner project scores.

[0039] In a third aspect, an embodiment of the present invention provides a computer device, comprising: a processor and a memory, wherein the processor is used to execute an employee performance evaluation program stored in the memory to implement the employee performance evaluation method described in the first aspect.

[0040] In a fourth aspect, an embodiment of the present invention provides a storage medium, including: the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the employee performance evaluation method described in the first aspect above.

[0041] The employee performance evaluation scheme provided by the embodiment of the present invention obtains the employee work data of all employees in the enterprise; constructs a collaborator network model based on the employee work data; assigns an initial weight to each collaborator node in the collaborator network model based on a preset rule, and calculates the mutual influence weight between the collaborator nodes; calculates the collaborator project score of each collaborator node based on the employee work data and the collaborator network model; and evaluates the employee performance of each employee in the enterprise based on the mutual influence weight between the collaborator nodes and the collaborator project score. This scheme can provide a comprehensive and objective employee performance evaluation tool for the enterprise, and can also be used in aspects such as team building, project allocation, and talent management, to help the enterprise better tap the potential of its employees, thereby enhancing the overall competitiveness of the enterprise. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A flowchart of an employee performance evaluation method provided by an embodiment of the present invention;

[0043] Figure 2 A flowchart of another employee performance evaluation method provided by an embodiment of the present invention;

[0044] Figure 3 A schematic diagram of a partner network provided by an embodiment of the present invention;

[0045] Figure 4 A schematic diagram of the structure of an employee performance evaluation device provided by an embodiment of the present invention;

[0046] Figure 5 A schematic diagram of the structure of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] To facilitate understanding of the embodiments of the present invention, specific embodiments will be further explained below in conjunction with the accompanying drawings. The embodiments do not constitute a limitation on the embodiments of the present invention.

[0049] Figure 1 A flow chart of an employee performance evaluation method provided by an embodiment of the present invention is as follows: Figure 1 As shown, the method specifically includes:

[0050] S11. Obtain the employee work data of all employees in the enterprise.

[0051] In the embodiment of the present invention, project data in which all employees in the enterprise participate are systematically collected; and employee cooperation data of all employees are determined based on all project data.

[0052] First, clarify the collection objectives and scope of employee work data, design appropriate survey tools and data sources. Next, formulate and implement a data collection plan to ensure the accuracy and completeness of the data. After that, verify and clean the data, and store and back it up securely. At the same time, comply with privacy policies and laws and regulations to ensure data security.

[0053] S12. Constructing a collaborator network model based on the employee work data.

[0054] Based on the employee cooperation data obtained above, a collaborator network model is constructed with employee information as the collaborator nodes of the collaborator network model and projects jointly participated by employees as the edges of the collaborator network model.

[0055] S13. Assign an initial weight to each partner node in the partner network model based on preset rules, and calculate the mutual influence weights between the partner nodes.

[0056] An initial weight is assigned to each collaborator node in the collaborator network model based on the number of projects each employee participates in; the mutual influence weights between the collaborator nodes are calculated based on the initial weight using the first formula and the second formula, where the first formula is: Among them, W(A l ,A k ) is the influence weight of partner node l on partner node k, (A l ,A k ) is the number of projects that partner node l and partner node k jointly collaborate on, PA k is the number of projects that partner node k participates in.

[0057] The second formula is: Among them, W(A k ,A l ) is the influence weight of partner node k on partner node l, (A k ,A l ) is the number of projects that partner node l and partner node k jointly collaborate on, PA l is the number of projects in which partner node l participates.

[0058] S14. Calculate the collaborator project score of each collaborator node based on the employee work data and the collaborator network model.

[0059] The project level is determined based on the project data in which each employee participates; the collaborator project score of each collaborator node is calculated by the third formula based on the pre-divided customer level, project level and collaborator network model. The third formula is: Among them, P is the project set, pub i For the i-th item, r(pub i ) is the item level, α is a parameter, and its value is (0<α<1).

[0060] S15. Evaluate the employee performance of each employee in the enterprise based on the mutual influence weights between the partner nodes and the partner project scores.

[0061] The project level is determined based on the project data in which each employee participates; the collaborator project score of each collaborator node is calculated by the third formula based on the pre-divided customer level, project level and collaborator network model. The third formula is: Among them, P is the project set, pub i For the i-th item, r(pub i ) is the item level, α is a parameter, and its value is (0<α<1).

[0062] Furthermore, based on the mutual influence weights between the collaborator nodes and the collaborator project scores, the PageRank algorithm is applied to calculate the final score of each employee through the fourth and fifth formulas. The fourth formula is: Wherein, d is the damping factor, and its value is (0≤d≤1).

[0063] The fifth formula is: Where n is the total number of partners, W(A i ,A j ) is the influence weight of partner node i on partner node j, λ(A i ) is the collaborator project score, X is the final score of each employee, W(Ak,Aj) is the influence weight of collaborator k on collaborator j, λ(A k ) is the final score of collaborator k.

[0064] Further, the employee performance of each employee is evaluated based on the final score of each employee.

[0065] Optionally, employee management can be performed based on employee performance for use in team building, project allocation, talent management, etc., to help companies better tap the potential of their employees and improve overall competitiveness.

[0066] The employee performance evaluation method provided by the embodiment of the present invention obtains the employee work data of all employees in the enterprise; constructs a collaborator network model based on the employee work data; assigns an initial weight to each collaborator node in the collaborator network model based on a preset rule, and calculates the mutual influence weight between the collaborator nodes; calculates the collaborator project score of each collaborator node based on the employee work data and the collaborator network model; and evaluates the employee performance of each employee in the enterprise based on the mutual influence weight between the collaborator nodes and the collaborator project score. This method can provide a comprehensive and objective employee performance evaluation tool for the enterprise, and can also be used in team building, project allocation, talent management and other aspects to help the enterprise better exert the potential of employees, thereby improving the overall competitiveness of the enterprise.

[0067] Figure 2 A flow chart of another employee performance evaluation method provided by an embodiment of the present invention is shown as follows: Figure 2 As shown, the method specifically includes:

[0068] S21. Obtain project data in which all employees in the enterprise participate.

[0069] In the embodiment of the present invention, project data in which all employees in the enterprise participate are systematically collected; and employee cooperation data of all employees are determined based on all project data.

[0070] First, clarify the collection objectives and scope of employee work data, design appropriate survey tools and data sources. Next, formulate and implement a data collection plan to ensure the accuracy and completeness of the data. After that, verify and clean the data, and store and back it up securely. At the same time, comply with privacy policies and laws and regulations to ensure data security.

[0071] S22. Determine employee cooperation data of all employees based on the project data.

[0072] Based on the project data of employee participation, it is possible to determine which employees have participated in the same or multiple projects, and further determine the employee cooperation data of all employees.

[0073] S23. Based on the employee cooperation data, a collaborator network model is constructed by using employee information as collaborator nodes of the collaborator network model and using projects jointly participated by employees as edges of the collaborator network model.

[0074] Collaborator network analysis is a method based on graph theory and social network analysis. It focuses on studying the relationship network formed by cooperation between individuals. Within an enterprise, activities such as cooperative projects, teamwork, and information sharing between employees can be regarded as a kind of cooperative behavior. By collecting and analyzing these behavioral data, a collaborator network within the enterprise can be constructed. This network can reflect the closeness of cooperation between employees, the flow of knowledge, and the potential distribution of influence.

[0075] Based on the employee collaboration data, a collaborator network model is constructed. In this network model, the collaborator nodes represent employees, and the edges represent the projects that employees jointly participate in. Through this network structure, it is possible to clearly see which employees frequently interact in project collaboration and which teams or departments have close collaboration relationships. For example, Figure 3 shown.

[0076] S24. Assign an initial weight to each collaborator node in the collaborator network model based on the number of projects each employee participates in.

[0077] S25. Calculate the mutual influence weights between the partner nodes based on the initial weights by using the first formula and the second formula.

[0078] When constructing the collaborator network model, each collaborator node is assigned an initial weight based on the number of projects each employee has participated in. This weight assignment method is based on quantitative indicators commonly used in academic research and aims to reflect the contribution and influence of individuals in the project. A high weight represents the employee's active participation and rich experience in the project, while a low weight may mean that the employee has unique but unfulfilled potential. Based on the fact that experienced collaborators will have a greater influence on less experienced collaborators, the mutual influence between collaborators is calculated.

[0079] The mutual influence weights between the partner nodes are calculated based on the initial weights through the first formula and the second formula. The first formula is: Among them, W(A l ,A k ) is the influence weight of partner node l on partner node k, (A l ,A k ) is the number of projects that partner node l and partner node k jointly collaborate on, PA k is the number of projects that partner node k participates in.

[0080] The second formula is: Among them, W(A k ,A l ) is the influence weight of partner node k on partner node l, (A k ,A l) is the number of projects that partner node l and partner node k jointly collaborate on, PA l is the number of projects in which partner node l participates.

[0081] For example, suppose there are two collaborators, collaborator K has 4 projects, and collaborator L has 3 projects. If collaborators K and L work together on 2 projects, the weight of their mutual influence is calculated as follows:

[0082]

[0083] Because collaborator K has more publications than collaborator L, collaborator K has a greater influence on collaborator L.

[0084] S26. Determine the project level based on the project data in which each employee participates.

[0085] S27. Calculate the partner project score of each partner node through a third formula based on the pre-divided customer level, the project level and the partner network model.

[0086] The project score of the collaborator is calculated based on the customer classification. For the calculation of the project score, the customer classification is taken into account. If an employee participates in more projects of large customers, then he will have a greater chance of becoming a higher performer. The level of the project is measured by customer classification, which is divided into four levels. That is, Class I customers (high-quality projects), Class II customers (important projects), Class III customers (reputable projects), and ordinary customers (general projects). The quality of the collaborator's project is calculated based on the project set P of collaborator i, and the score is as follows.

[0087]

[0088] Among them, pub i For the i-th item, r(pub i ) is the object level, and the α value is (0<α<1). i The larger it is, the higher the average quality of the projects that collaborators participate in.

[0089] In different usage environments, the value of the α parameter often needs to be determined through experimental calculations. This is because different environments have different requirements and influences on the α parameter. Only through experiments can the α parameter value that best suits the current environment be found. Through experimental strips, observe and analyze the impact of α parameter changes on system performance or experimental results, so as to select the best α parameter.

[0090] S28. Based on the mutual influence weights between the partner nodes and the partner project scores, the PageRank algorithm is applied to calculate the final score of each employee through the fourth formula and the fifth formula.

[0091] As a classic web page ranking algorithm, the core idea of ​​PageRank algorithm is to evaluate the importance of nodes (web pages in web page ranking and employees in employee performance evaluation) by simulating the random walk process in the network. PageRank algorithm believes that the importance of a node depends on the importance and number of other nodes pointing to it. In the internal partner network of an enterprise, cooperation and recommendation between employees can be regarded as a kind of "voting" behavior, that is, employees tend to cooperate with or recommend colleagues they think are capable. Therefore, PageRank algorithm can be well applied to evaluate the influence and ability of employees in the partner network.

[0092] In the embodiment of the present invention, based on the mutual influence weights between the collaborator nodes and the collaborator project scores, the PageRank algorithm is applied to calculate the final score of each employee through the fourth formula and the fifth formula. The fourth formula is: Wherein, d is the damping factor, and its value is (0≤d≤1).

[0093] The fifth formula is: Where n is the total number of partners, W(A i ,A j ) is the influence weight of partner node i on partner node j, λ(A i ) is the collaborator project score, X is the final score of each employee, W(Ak,Aj) is the influence weight of collaborator k on collaborator j, λ(A k ) is the final score of collaborator k.

[0094] S29. Evaluate the employee performance of each employee based on their final score.

[0095] S210: Perform employee management based on the employee performance.

[0096] According to the final score calculated by the PageRank algorithm, the employees are accurately ranked. After the above steps, the performance calculation of the company's employees can be realized.

[0097] In addition to performance evaluation, this method can also be used in team building, project allocation, talent management, etc., to help companies better tap the potential of their employees and improve overall competitiveness.

[0098] The employee performance evaluation method provided by the embodiment of the present invention obtains the employee work data of all employees in the enterprise; constructs a collaborator network model based on the employee work data; assigns an initial weight to each collaborator node in the collaborator network model based on a preset rule, and calculates the mutual influence weight between the collaborator nodes; calculates the collaborator project score of each collaborator node based on the employee work data and the collaborator network model; and evaluates the employee performance of each employee in the enterprise based on the mutual influence weight between the collaborator nodes and the collaborator project score. This method can provide a comprehensive and objective employee performance evaluation tool for the enterprise, and can also be used in team building, project allocation, talent management and other aspects to help the enterprise better exert the potential of employees, thereby improving the overall competitiveness of the enterprise.

[0099] Figure 4 A schematic diagram of the structure of an employee performance evaluation device provided by an embodiment of the present invention specifically includes:

[0100] The acquisition module 401 is used to acquire the employee work data of all employees in the enterprise. For detailed description, please refer to the relevant description corresponding to the above method embodiment, which will not be repeated here.

[0101] The construction module 402 is used to construct a collaborator network model based on the employee work data. For detailed description, please refer to the relevant description corresponding to the above method embodiment, which will not be repeated here.

[0102] The calculation module 403 is used to assign an initial weight to each partner node in the partner network model based on a preset rule, and calculate the mutual influence weights between the partner nodes. For detailed description, please refer to the relevant description corresponding to the above method embodiment, which will not be repeated here.

[0103] The calculation module 403 is used to calculate the collaborator project score of each collaborator node based on the employee work data and the collaborator network model. For detailed description, please refer to the relevant description corresponding to the above method embodiment, which will not be repeated here.

[0104] The evaluation module 404 is used to evaluate the employee performance of each employee in the enterprise based on the mutual influence weights between the partner nodes and the partner project scores. For detailed descriptions, please refer to the relevant descriptions corresponding to the above method embodiments, which will not be repeated here.

[0105] The employee performance evaluation device provided in this embodiment can be as follows Figure 4 The employee performance evaluation device shown in can be executed as follows Figure 1-2 All steps of employee performance evaluation method in order to achieve Figure 1-2 For details, please refer to the technical effects of the employee performance evaluation method shown in Figure 1-2 For the sake of brevity, the relevant description is not repeated here.

[0106] Figure 5 A schematic diagram of the structure of a computer device provided by an embodiment of the present invention, Figure 5 The computer device 500 shown includes: at least one processor 501, a memory 502, at least one network interface 504 and other user interfaces 503. The various components in the computer device 500 are coupled together via a bus system 505. It is understood that the bus system 505 is used to achieve connection and communication between these components. In addition to the data bus, the bus system 505 also includes a power bus, a control bus and a status signal bus. However, for the sake of clarity, the bus system 505 is not described in detail. Figure 5 Various buses are labeled as bus system 505 .

[0107] The user interface 503 may include a display, a keyboard, or a pointing device (eg, a mouse, a trackball, a touch pad, or a touch screen).

[0108] It can be understood that the memory 502 in the embodiment of the present invention can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM) and direct RAM bus random access memory (DRRAM). The memory 502 described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0109] In some implementations, the memory 502 stores the following elements, executable units or data structures, or a subset thereof, or an extended set thereof: an operating system 5021 and an application program 5022 .

[0110] The operating system 5021 includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., which are used to implement various basic services and process hardware-based tasks. The application 5022 includes various application programs, such as a media player (Media Player), a browser (Browser), etc., which are used to implement various application services. The program for implementing the method of the embodiment of the present invention can be included in the application 5022.

[0111] In the embodiment of the present invention, by calling the program or instruction stored in the memory 502, specifically, the program or instruction stored in the application 5022, the processor 501 is used to execute the method steps provided by each method embodiment, for example, including:

[0112] Acquire employee work data of all employees in the enterprise; construct a collaborator network model based on the employee work data; assign an initial weight to each collaborator node in the collaborator network model based on preset rules, and calculate the mutual influence weight between the collaborator nodes; calculate the collaborator project score of each collaborator node based on the employee work data and the collaborator network model; evaluate the employee performance of each employee in the enterprise based on the mutual influence weight between the collaborator nodes and the collaborator project score.

[0113] In a possible implementation, project data in which all employees of the enterprise participate is obtained; and employee cooperation data of all employees is determined based on the project data.

[0114] In a possible implementation, based on the employee cooperation data, a collaborator network model is constructed with employee information as collaborator nodes of the collaborator network model and with projects in which employees participate as edges of the collaborator network model.

[0115] In a possible implementation, an initial weight is assigned to each collaborator node in the collaborator network model based on the number of projects in which each employee participates; and the mutual influence weights between the collaborator nodes are calculated based on the initial weight using a first formula and a second formula, wherein the first formula is: Among them, W(A l ,A k ) is the influence weight of partner node l on partner node k, (A l ,A k ) is the number of projects that partner node l and partner node k jointly collaborate on, PA kis the number of projects in which the partner node k participates; the second formula is: Among them, W(A k ,A l ) is the influence weight of partner node k on partner node l, (A k ,A l ) is the number of projects that partner node l and partner node k jointly collaborate on, PA l is the number of projects in which partner node l participates.

[0116] In one possible implementation, the project level is determined based on the project data in which each employee participates; the collaborator project score of each collaborator node is calculated by a third formula based on the pre-divided customer level, the project level and the collaborator network model, and the third formula is: Among them, P is the project set, pub i For the i-th item, r(pub i ) is the item level, α is a parameter, and its value is (0<α<1).

[0117] In a possible implementation, based on the mutual influence weights between the partner nodes and the partner project scores, the PageRank algorithm is applied to calculate the final score of each employee through the fourth formula and the fifth formula. The fourth formula is: Wherein, d is the damping factor, and its value is (0≤d≤1); the fifth formula is: Where n is the total number of partners, W(A i ,A j ) is the influence weight of partner node i on partner node j, λ(A i ) is the collaborator project score, X is the final score of each employee, W(Ak,Aj) is the influence weight of collaborator k on collaborator j, λ(A k ) is the final score of collaborator k; the employee performance of each employee is evaluated based on the final score of each employee.

[0118] In a possible implementation, employee management is performed based on the employee performance.

[0119] The method disclosed in the above embodiment of the present invention can be applied to the processor 501, or implemented by the processor 501. The processor 501 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit in the processor 501 or the instruction in the form of software. The above processor 501 can be a general processor, a digital signal processor (Digital Signal Processor, DSP), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a field programmable gate array (Field Programmable Gate Array, FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiment of the present invention can be directly embodied as a hardware decoding processor to execute, or the hardware and software units in the decoding processor can be executed. The software unit can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 502, and the processor 501 reads the information in the memory 502 and completes the steps of the above method in combination with its hardware.

[0120] It is understood that the embodiments described herein may be implemented in hardware, software, firmware, middleware, microcode, or a combination thereof. For hardware implementation, the processing unit may be implemented in one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPDevice, DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), general purpose processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions described in the present application, or a combination thereof.

[0121] For software implementation, the technology described herein can be implemented by a unit that performs the functions described herein. The software code can be stored in a memory and executed by a processor. The memory can be implemented in the processor or outside the processor.

[0122] The computer device provided in this embodiment may be as follows Figure 5 The computer device shown in , can execute Figure 1-2 All steps of employee performance evaluation method in order to achieve Figure 1-2 For details, please refer to the technical effects of the employee performance evaluation method shown in Figure 1-2 For the sake of brevity, the relevant description is not repeated here.

[0123] The embodiment of the present invention also provides a storage medium (computer-readable storage medium). The storage medium here stores one or more programs. The storage medium may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a read-only memory, a flash memory, a hard disk or a solid-state drive; the memory may also include a combination of the above-mentioned types of memory.

[0124] When one or more programs in the storage medium can be executed by one or more processors, the employee performance evaluation method executed on the computer device side can be implemented.

[0125] The processor is used to execute the employee performance evaluation program stored in the memory to implement the following steps of the employee performance evaluation method executed on the computer device side:

[0126] Acquire employee work data of all employees in the enterprise; construct a collaborator network model based on the employee work data; assign an initial weight to each collaborator node in the collaborator network model based on preset rules, and calculate the mutual influence weight between the collaborator nodes; calculate the collaborator project score of each collaborator node based on the employee work data and the collaborator network model; evaluate the employee performance of each employee in the enterprise based on the mutual influence weight between the collaborator nodes and the collaborator project score.

[0127] In a possible implementation, project data in which all employees of the enterprise participate is obtained; and employee cooperation data of all employees is determined based on the project data.

[0128] In a possible implementation, based on the employee cooperation data, a collaborator network model is constructed with employee information as collaborator nodes of the collaborator network model and with projects in which employees participate as edges of the collaborator network model.

[0129] In a possible implementation, an initial weight is assigned to each collaborator node in the collaborator network model based on the number of projects in which each employee participates; and the mutual influence weights between the collaborator nodes are calculated based on the initial weight using a first formula and a second formula, wherein the first formula is: Among them, W(A l ,A k) is the influence weight of partner node l on partner node k, (A l ,A k ) is the number of projects that partner node l and partner node k jointly collaborate on, PA k is the number of projects in which the partner node k participates; the second formula is: Among them, W(A k ,A l ) is the influence weight of partner node k on partner node l, (A k ,A l ) is the number of projects that partner node l and partner node k jointly collaborate on, PA l is the number of projects in which partner node l participates.

[0130] In one possible implementation, the project level is determined based on the project data in which each employee participates; the collaborator project score of each collaborator node is calculated by a third formula based on the pre-divided customer level, the project level and the collaborator network model, and the third formula is: Among them, P is the project set, pub i For the i-th item, r(pub i ) is the item level, α is a parameter, and its value is (0<α<1).

[0131] In a possible implementation, based on the mutual influence weights between the partner nodes and the partner project scores, the PageRank algorithm is applied to calculate the final score of each employee through the fourth formula and the fifth formula. The fourth formula is: Wherein, d is the damping factor, and its value is (0≤d≤1); the fifth formula is: Where n is the total number of partners, W(A i ,A j ) is the influence weight of partner node i on partner node j, λ(A i ) is the collaborator project score, X is the final score of each employee, W(Ak,Aj) is the influence weight of collaborator k on collaborator j, λ(A k ) is the final score of collaborator k; the employee performance of each employee is evaluated based on the final score of each employee.

[0132] In a possible implementation, employee management is performed based on the employee performance.

[0133] The professionals should further realize that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to the function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0134] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0135] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for evaluating employee performance, characterized in that: include: Obtain employee work data of all employees in the enterprise; The obtaining of employee work data of all employees in the enterprise includes: Obtain project data of all employees in the enterprise; determining employee cooperation data for all employees based on the project data; Building a collaborator network model based on the employee work data; The constructing of a collaborator network model based on the employee work data includes: Based on the employee cooperation data, a collaborator network model is constructed by using employee information as collaborator nodes of the collaborator network model and using projects jointly participated by employees as edges of the collaborator network model; Assign initial weights to each partner node in the partner network model based on preset rules, and calculate the mutual influence weights between partner nodes; Calculating a collaborator project score of each collaborator node based on the employee work data and the collaborator network model; The employee performance of each employee in the enterprise is evaluated based on the mutual influence weights between the partner nodes and the partner project scores.

2. The method according to claim 1, characterized in that The method of allocating an initial weight to each partner node in the partner network model based on a preset rule and calculating the mutual influence weights between the partner nodes includes: Assign an initial weight to each collaborator node in the collaborator network model based on the number of projects each employee participates in; The mutual influence weights between the partner nodes are calculated based on the initial weights by using the first formula and the second formula, where the first formula is: Among them, W(A l ,A k ) is the influence weight of partner node l on partner node k, (A l ,A k ) is the number of projects that partner node l and partner node k jointly collaborate on, PA k is the number of projects in which the collaborator node k participates; The second formula is: Among them, W(A k ,A l ) is the influence weight of partner node k on partner node l, (A k ,A l ) is the number of projects that partner node l and partner node k jointly collaborate on, PA l is the number of projects in which partner node l participates.

3. The method according to claim 2, characterized in that The calculating the collaborator project score of each collaborator node based on the employee work data and the collaborator network model includes: Determine project levels based on project data for each employee; The partner project score of each partner node is calculated by a third formula based on the pre-divided customer level, the project level and the partner network model. The third formula is: Among them, P is the project set, pub i For the i-th item, r(pub i ) is the item level, α is a parameter, and its value is 0<α<1.

4. The method according to claim 3, characterized in that The evaluating the employee performance of each employee in the enterprise based on the mutual influence weights between the partner nodes and the partner project scores includes: Based on the mutual influence weights between the collaborator nodes and the collaborator project scores, the PageRank algorithm is applied to calculate the final score of each employee through the fourth and fifth formulas. The fourth formula is: Where, d is the damping factor, and its value is 0≤d≤1; The fifth formula is: Where n is the total number of partners, W(A i ,A j ) is the influence weight of partner node i on partner node j, λ(A i ) is the collaborator project score, X is the final score of each employee, W(A k ,A j ) is the influence weight of collaborator k on collaborator j, λ(A k ) is the final score of collaborator k; Evaluate employee performance of each employee based on their final score.

5. The method according to claim 4, characterized in that The method further comprises: Employee management is performed based on said employee performance.

6. An employee performance evaluation device, characterized in that: include: The acquisition module is used to obtain the employee work data of all employees in the enterprise; The obtaining of employee work data of all employees in the enterprise includes: obtaining project data in which all employees of the enterprise participate; and determining employee cooperation data of all employees based on the project data; A construction module, used to construct a collaborator network model based on the employee work data; the construction of the collaborator network model based on the employee work data includes: based on the employee cooperation data, using employee information as collaborator nodes of the collaborator network model, and using projects jointly participated by employees as edges of the collaborator network model to construct the collaborator network model; A calculation module, used to assign an initial weight to each partner node in the partner network model based on a preset rule, and calculate the mutual influence weights between the partner nodes; A calculation module, used for calculating a collaborator project score of each collaborator node based on the employee work data and the collaborator network model; An evaluation module is used to evaluate the employee performance of each employee in the enterprise based on the mutual influence weights between the partner nodes and the partner project scores.

7. A computer device, characterized in that: include: A processor and a memory, wherein the processor is used to execute an employee performance evaluation program stored in the memory to implement the employee performance evaluation method according to any one of claims 1 to 5.

8. A storage medium, characterized in that: The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the employee performance evaluation method according to any one of claims 1 to 5.

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