Work performance evaluation method and device for enterprise manager, and medium
By collecting, preprocessing and standardizing multi-dimensional work data of enterprise managers and employees, using linear regression model and loss function to build parameter sets, combining the position-weight mapping relationship, calculating and visualizing the manager KPI value, the problem of inaccurate evaluation in the existing technology is solved, and the objectification, refinement and dynamic evaluation of manager performance is achieved.
Patent Information
- Application Number
- CN202510673995.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-23
- Publication Date
- 2025-08-15
Smart Images
Figure CN120494630A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of enterprise manager work evaluation, and in particular to a work performance evaluation method, device and medium for enterprise managers. Background Art
[0002] In today's management evaluation field, quantitative assessment of managers and their teams is a crucial topic. Traditional manager evaluation methods often combine comprehensive subjective scores with some objective data. These methods fail to comprehensively and systematically collect multi-dimensional raw work data from each employee within the management team. As a result, the data foundation is not solid and complete, and cannot fully reflect the quality of the manager's work.
[0003] Furthermore, with the development of information technology, companies can access vast amounts of employee performance data through information systems. However, extracting effective features from this massive data and constructing scientific evaluation models remains a technical challenge. Existing solutions attempt to use simple weighted or linear regression analysis for evaluation, but these results fail to accurately reflect managers' true abilities in leading teams, integrating resources, and achieving organizational goals, making them unable to meet the increasingly complex and sophisticated demands of modern management evaluation.
[0004] Therefore, how to evaluate the work performance of managers based on the work data of enterprise managers and employees has become a technical problem that needs to be solved urgently. Summary of the Invention
[0005] The embodiments of the present application provide a work performance evaluation method, device and medium for enterprise managers, which are used to solve the technical problem of how to evaluate the work performance of managers based on the work data of enterprise managers' employees.
[0006] In the first aspect, an embodiment of the present application provides a work performance evaluation method for enterprise managers, the method comprising: collecting multidimensional original work data of each employee in the manager team to be evaluated, and preprocessing the multidimensional original work data to obtain multidimensional standard work data of each employee; using a preset loss function to process the multidimensional standard work data to construct a linear regression model parameter set for each employee; based on the linear regression model parameter set, performing weighted calculation on the multidimensional standard work data of each employee to obtain a first KPI value for each employee; based on a preset position-weight mapping relationship, performing weighted calculation on the first KPI value to obtain a second KPI value for the manager to be evaluated; and performing statistical analysis and visual display on the second KPI value.
[0007] In one implementation of the present application, a preset loss function is used to process multidimensional standard work data to construct a linear regression model parameter set for each employee, specifically including: constructing initialized linear regression model parameters; based on the preset loss function, iteratively updating the initialized linear regression model parameter set using a gradient descent algorithm; when the initialized linear regression model parameter set is iterated until the loss function converges, it is used as the linear regression model parameter set to be applied.
[0008] In one implementation of the present application, the loss function is represented by the following formula:
[0009]
[0010] θ=(θ0,θ1,…,θ d )
[0011]
[0012] Among them, J(θ) represents the loss function, x i represents the i-th employee in the manager team to be evaluated, h θ (x i ) represents the KPI value of the i-th employee during the iteration process, y i represents the preset KPI target value corresponding to the i-th employee, θ represents the initialized linear regression model parameter set, d represents the dimension of the multidimensional standard work data and the number of model parameters in the initialized linear regression model parameter set, and n represents the number of employees in the manager team to be evaluated.
[0013] In one implementation of the present application, based on a linear regression model parameter set, a weighted calculation is performed on the multidimensional standard work data of each employee to obtain the first KPI value of each employee, specifically including: combining each model parameter in the linear regression model parameter set with the corresponding work data in the multidimensional standard work data to obtain the basic contribution value of the employee to the work task corresponding to the work data; summing up all basic contribution values corresponding to the multidimensional standard work data to obtain the first KPI value.
[0014] In one implementation of the present application, a position-weight mapping relationship is constructed, specifically including: obtaining the job responsibility requirements of each position within the management team to be evaluated, and based on the job responsibility requirements, calculating the job weight of the corresponding position through the preset weight calculation logic; the job weight is matched one-to-one with the position of each employee in the management team to be evaluated to obtain a position-weight mapping relationship.
[0015] In one implementation of the present application, based on the job responsibilities, the job weight of the corresponding position is calculated through a preset weight calculation logic, specifically including: decomposing the job responsibilities into several task items, and constructing a hierarchical evaluation system for task items based on the enterprise operation goals; evaluating the importance of each task item, and generating an importance index of each task item in the team of managers to be evaluated; based on the importance index corresponding to all task items of each position, determining the sum of the importance index corresponding to each position; based on the sum of the importance index corresponding to each position, determining the job weight of each position in the team of managers to be evaluated.
[0016] In one implementation of the present application, the second KPI value is statistically analyzed and visually displayed, specifically including: performing a time dimension comparative analysis on the second KPI value to generate a performance trend report containing changes in the second KPI value within a historical period; generating a dynamic chart based on the performance trend report, and sending the performance trend report and the dynamic chart to a display device to achieve visual display; wherein the dynamic chart includes at least a line chart and / or a heat map and / or a radar chart.
[0017] In one implementation of the present application, the multidimensional original work data of each employee in the management team to be evaluated is collected, and the multidimensional original work data is preprocessed, specifically including: eliminating outliers from the multidimensional original work data of each employee and filling in missing values; using the z-score standardization method to standardize the multidimensional original work data of each employee to eliminate the dimension of the multidimensional original work data of each employee and obtain standardized data.
[0018] In a second aspect, an embodiment of the present application also provides a work performance evaluation device for enterprise managers, the device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a method such as any one of the above items.
[0019] In a third aspect, an embodiment of the present application further provides a non-volatile computer storage medium for evaluating the work performance of enterprise managers, which stores computer-executable instructions. When the computer-executable instructions are executed, a method such as any one of the above is implemented.
[0020] The embodiments of the present application provide a work performance evaluation method, device and medium for enterprise managers, which automatically optimizes the weights of each dimension through a linear regression model and a preset loss function, and uses a linear regression model parameter set to clearly display the degree of influence of each dimension on the KPI, so as to facilitate managers to understand the evaluation logic and improve the team's shortcomings in a targeted manner; through the position-weight mapping relationship, the employee KPI is weighted and aggregated into the manager KPI according to the job characteristics, reflecting the differentiated contributions of managers to different functional teams, and realizing the objectivity, refinement and dynamism of the manager performance evaluation. BRIEF DESCRIPTION OF THE DRAWINGS
[0021] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:
[0022] Figure 1 A flowchart of a work performance evaluation method for enterprise managers provided in an embodiment of the present application;
[0023] Figure 2 A schematic diagram of the internal structure of a work performance evaluation device for enterprise managers provided in an embodiment of the present application. DETAILED DESCRIPTION
[0024] To make the purpose, technical solutions, and advantages of this application more clear, the technical solutions of this application will be clearly and completely described below in conjunction with the specific embodiments of this application and the corresponding drawings. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.
[0025] The embodiments of the present application provide a work performance evaluation method, device and medium for enterprise managers, which are used to solve the technical problem of how to evaluate the work performance of managers based on the work data of enterprise managers' employees.
[0026] The technical solutions proposed in the embodiments of the present application are described in detail below with reference to the accompanying drawings.
[0027] Figure 1 This is a flow chart of a work performance evaluation method for enterprise managers provided in the embodiment of this application. Figure 1 As shown, the embodiment of the present application provides a work performance evaluation method for enterprise managers, which specifically includes the following steps:
[0028] Step 10: Collect the multidimensional original work data of each employee in the management team to be evaluated, and pre-process the multidimensional original work data to obtain the multidimensional standard work data of each employee.
[0029] As an optional embodiment, the multidimensional original work data of each employee in the management team to be evaluated is collected, and the multidimensional original work data is preprocessed to obtain the multidimensional standard work data of each employee. Specifically, it may include: Step 101: eliminating outliers from the multidimensional original work data of each employee and filling in missing values.
[0030] In this step, the multidimensional original work data of each employee covers the records and reflections of the employee's work situation in different aspects. The collected multidimensional original work data of employees are preprocessed. First, all the original work data are checked to identify and process the outliers. Outliers refer to data points that obviously deviate from the overall level of the rest of the data and do not conform to the normal working conditions and data distribution patterns. Statistical methods are used to identify abnormal data points and remove them from the data set, retaining relatively reasonable data that can truly reflect the normal working conditions of employees. At the same time, in the process of collecting the multidimensional original work data of employees, if data is missing due to data recording errors, untimely collection, etc., the missing data points can be replaced by calculating the mean of all employee data under this dimension, or the median can be used to fill in the gaps. The values of the missing data positions can be restored in a reasonable way, so that the integrity of the entire employee multidimensional work data set is improved for better subsequent analysis and utilization.
[0031] Step 102: The multi-dimensional original work data of each employee is standardized using a z-score standardization method to eliminate the dimension of the multi-dimensional original work data of each employee and obtain standardized data.
[0032] In this step, after completing the preliminary data cleaning and filling, the z-score standardization method is used to standardize the multi-dimensional original work data of each employee. First, the average and standard deviation of the original data values of each dimension of each employee must be calculated, and the difference between each data point and the average value of the dimension data is divided by the standard deviation of the dimension data to obtain the standardized data value. The z-score standardization method is used to eliminate the data dimensions of each dimension. In the subsequent comprehensive analysis, comparative evaluation, model building and other operations of the employee work data, the employee work characteristics represented by each dimension data can be objectively considered to avoid unreasonable situations caused by dimensional differences, thereby obtaining more scientific, accurate and reliable analysis results and decision-making basis.
[0033] Step 20: Use the preset loss function to process the multi-dimensional standard work data to construct a linear regression model parameter set for each employee.
[0034] As an optional embodiment, the multi-dimensional standard work data is processed using a preset loss function to construct a linear regression model parameter set for each employee, which may specifically include: step 201: constructing the initialized linear regression model parameters; step 202: based on the preset loss function, using the gradient descent algorithm to iteratively update the initialized linear regression model parameter set.
[0035] In this step, the preset loss function is expressed by the following formula:
[0036]
[0037] θ=(θ0,θ1,…,θ d )
[0038]
[0039] Among them, J(θ) represents the loss function, x i represents the i-th employee in the manager team to be evaluated, h θ (x i ) represents the KPI value of the i-th employee during the iteration process, y i represents the preset KPI target value corresponding to the i-th employee, θ represents the initialized linear regression model parameter set, d represents the dimension of the multidimensional standard work data and the number of model parameters in the initialized linear regression model parameter set, and n represents the number of employees in the manager team to be evaluated.
[0040] The loss function is an important metric that measures the difference between the model's predicted values and the true values. For linear regression models, the mean squared error (MSE) is often used as the loss function. It calculates the average of the squares of the differences between the predicted values and the true values. By minimizing this loss function, the model's predictions can be made as close as possible to the actual observed values.
[0041] Gradient descent is a commonly used optimization algorithm. Its core idea is to determine the direction and magnitude of parameter updates by calculating the gradient (i.e., derivative) of the loss function with respect to the model parameters. In each iteration, the gradient under the current model parameters is first calculated. This gradient points to the direction in which the loss function increases fastest. In order to minimize the loss function, the parameters need to be updated in the opposite direction of the gradient.
[0042] At the initial stage of linear regression model construction, it is necessary to randomly assign initial values to its parameters, and use the gradient descent algorithm to iteratively update the initialized linear regression model parameter set; after each parameter update, it is necessary to calculate the new loss function value and the new gradient value, and compare them with the previous loss function value, and move forward a step in the direction of the gradient. The step size gradually decreases with the increase in the number of iterations, which helps to balance the convergence speed, avoid falling into local optimality, and improve the model training effect.
[0043] Step 203: When the initial linear regression model parameter set is iterated until the loss function converges, it is used as the linear regression model parameter set to be applied.
[0044] In this step, the gradient calculation and the step size are repeated until the loss function converges to obtain the optimized parameters. This set of parameters is used as the model parameter set to be applied. At this time, the difference between the model prediction value and the true value is minimized, which represents an ideal model parameter combination obtained through a reasonable training process under the conditions of the current training data set, the selected model structure and the optimization algorithm.
[0045] Step 30: Based on the linear regression model parameter set, perform weighted calculation on the multi-dimensional standard work data of each employee to obtain the first KPI value of each employee.
[0046] As an optional embodiment, based on the linear regression model parameter set, a weighted calculation is performed on the multidimensional standard work data of each employee to obtain the first KPI value of each employee, which may specifically include: Step 301: combining each model parameter in the linear regression model parameter set with the corresponding work data in the multidimensional standard work data to obtain the basic contribution value of the employee to the work task corresponding to the work data; Step 302: summing all basic contribution values corresponding to the multidimensional standard work data to obtain the first KPI value.
[0047] In this step, the KPI value of each employee in the manager team to be evaluated is obtained by weighted summation using the following formula:
[0048]
[0049] Among them, θ (i) The parameter set of the linear regression model to be applied after continuous iteration and update through the gradient descent algorithm, KPI i The KPI value of each employee in the manager team to be evaluated.
[0050] Step 40: Based on the preset position-weight mapping relationship, perform weighted calculation on the first KPI value to obtain a second KPI value of the manager to be evaluated.
[0051] As an optional embodiment, based on a preset position-weight mapping relationship, weighted calculation is performed on the first KPI value to obtain a second KPI value of the manager to be evaluated, which may specifically include: Step 401: constructing a position-weight mapping relationship.
[0052] As an optional embodiment, constructing a position-weight mapping relationship may specifically include: Step 4011: obtaining the job responsibility requirements of each position in the manager team to be evaluated, and based on the job responsibility requirements, calculating the job weight of the corresponding position through a preset weight calculation logic.
[0053] In this step, you first need to comprehensively collect the job responsibilities of each position in the management team. Job responsibilities include a specific description of the job tasks, work objectives, and work scope of the position. It is also necessary to clarify many factors such as the resource input, skill level, work complexity, and contribution to the overall team goals required for each position in the actual work. Comprehensively evaluate the relevant factors of each job responsibility to design the weight calculation logic, and calculate the weight of the corresponding position based on the obtained job responsibilities.
[0054] Step 4012: Match the job weights to the jobs of the employees in the manager team to be evaluated one by one to obtain a job-weight mapping relationship.
[0055] Step 402: Based on job responsibilities, calculate the job weight of the corresponding job using a preset weight calculation logic.
[0056] As an optional embodiment, based on job responsibilities, the job weight of the corresponding position is calculated through a preset weight calculation logic, which may specifically include: Step 4021: decomposing the job responsibilities into several task items, and constructing a hierarchical evaluation system for the task items based on the enterprise operation goals.
[0057] In this step, first, based on the daily work descriptions of different positions, the major responsibilities are subdivided into several task items. Each task item should be specific to the operational level that can be directly measured and evaluated. According to the company's operating goals, the hierarchical structure of evaluation indicators is determined, and the weight and priority of each task item at different levels, as well as the collaborative relationship between task items at each level, are clarified, thus forming a complete and well-organized task item evaluation system.
[0058] Step 4022: Evaluate the importance of each task item and generate an importance index for each task item in the manager team to be evaluated.
[0059] In this step, expert scoring methods or data analysis methods are used to deeply analyze the responsibilities of each task item in the management team to be evaluated and the size and criticality of its role in achieving the overall operational goals of the enterprise. An importance index is generated for each task item, which intuitively reflects the differences in priority and influence of different task items in the team's work. Managers can more clearly understand the importance ranking of each task item in the team, and also provide a basis for subsequent quantitative analysis.
[0060] Step 4023: Based on the importance indexes corresponding to all task items of each position, determine the sum of the importance indexes corresponding to each position; Step 4024: Based on the sum of the importance indexes corresponding to each position, determine the position weight of each position in the management team to be evaluated.
[0061] Furthermore, the importance indexes of all task items of each position are added together to obtain the sum of the importance indexes corresponding to the position, which reflects the comprehensive importance of the position in the overall work tasks. Based on the sum of the position importance indexes, the position weight of each position in the team of managers to be evaluated is calculated, and the KPI value of the manager to be evaluated is calculated according to the following formula:
[0062]
[0063] Among them, ω i The weight of each position in the team of managers to be evaluated, KPI i The first KPI value is the KPI value of each employee in the team of the manager to be evaluated, and the second KPI value is the KPI value of the manager to be evaluated.
[0064] Step 50: Perform statistical analysis and visual display on the second KPI value.
[0065] As an optional embodiment, statistical analysis and visual display of the second KPI value may specifically include: Step 501: performing time dimension comparative analysis on the second KPI value to generate a performance trend report including changes in the second KPI value within a historical period.
[0066] In this step, the KPI data of managers from different periods are collected and arranged in chronological order. By comparing the data in adjacent time periods or the same cycle (such as month, quarter, and year), the increase or decrease, change trend, etc. are analyzed to generate a performance trend report. This report can intuitively show the fluctuations of the manager's KPI data in the historical period.
[0067] Step 502: Generate a dynamic chart based on the performance trend report, and send the performance trend report and the dynamic chart to a display device for visual display; wherein the dynamic chart includes at least a line chart and / or a heat map and / or a radar chart.
[0068] In this step, dynamic charts are generated based on the performance trend report. Data visualization tools are used to convert it into dynamic chart forms, including line charts, heat maps, radar charts, etc., and sent to the corresponding display devices, so that relevant managers or decision makers can understand the dynamic development trends of the business in the corresponding aspects and provide a reference basis based on time evolution for subsequent decisions.
[0069] The above is an embodiment of the method proposed in this application. Based on the same inventive concept, this application embodiment also provides a work performance evaluation device for enterprise managers, whose structure is as follows: Figure 2 shown.
[0070] Figure 2 This is a schematic diagram of the internal structure of a work performance evaluation device for enterprise managers provided in an embodiment of the present application. Figure 2 As shown, the equipment includes:
[0071] at least one processor 201;
[0072] and, a memory 202 communicatively coupled to the at least one processor;
[0073] The memory 202 stores instructions that can be executed by at least one processor, and the instructions are executed by the at least one processor 201 to enable the at least one processor 201 to:
[0074] Collect the multi-dimensional original work data of each employee in the management team to be evaluated, and pre-process the multi-dimensional original work data to obtain the multi-dimensional standard work data of each employee;
[0075] Using the preset loss function, the multi-dimensional standard work data is processed to construct the linear regression model parameter set for each employee;
[0076] Based on the linear regression model parameter set, weighted calculation is performed on the multi-dimensional standard work data of each employee to obtain the first KPI value of each employee;
[0077] Based on the preset position-weight mapping relationship, the first KPI value is weighted to obtain the second KPI value of the manager to be evaluated;
[0078] Perform statistical analysis and visual display on the second KPI value.
[0079] Some embodiments of the present application provide corresponding Figure 1 A non-volatile computer storage medium for evaluating the work performance of enterprise managers stores computer executable instructions, wherein the computer executable instructions are set to:
[0080] Collect the multi-dimensional original work data of each employee in the management team to be evaluated, and pre-process the multi-dimensional original work data to obtain the multi-dimensional standard work data of each employee;
[0081] Using the preset loss function, the multi-dimensional standard work data is processed to construct the linear regression model parameter set for each employee;
[0082] Based on the linear regression model parameter set, weighted calculation is performed on the multi-dimensional standard work data of each employee to obtain the first KPI value of each employee;
[0083] Based on the preset position-weight mapping relationship, the first KPI value is weighted to obtain the second KPI value of the manager to be evaluated;
[0084] Perform statistical analysis and visual display on the second KPI value.
[0085] The various embodiments in this application are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other. Each embodiment focuses on the differences from the other embodiments. In particular, the IoT device and media embodiments are generally similar to the method embodiments, so their description is relatively simple. For relevant portions, refer to the description of the method embodiments.
[0086] The system and medium provided in the embodiments of the present application correspond one-to-one to the method. Therefore, the system and medium also have similar beneficial technical effects to their corresponding methods. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the system and medium will not be repeated here.
[0087] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.
[0088] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.
[0089] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.
[0090] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.
[0091] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.
[0092] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.
[0093] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules, 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 disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.
[0094] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.
[0095] The foregoing is merely an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.
Claims
1. A work performance evaluation method for enterprise managers, characterized by: The method comprises: Collecting multidimensional original work data of each employee in the management team to be evaluated, and preprocessing the multidimensional original work data to obtain multidimensional standard work data of each employee; Processing the multidimensional standard work data using a preset loss function to construct a linear regression model parameter set for each employee; Based on the linear regression model parameter set, performing weighted calculation on the multi-dimensional standard work data of each employee to obtain a first KPI value of each employee; Based on a preset position-weight mapping relationship, weighted calculation is performed on the first KPI value to obtain a second KPI value of the manager to be evaluated; The second KPI value is statistically analyzed and visualized.
2. A work performance evaluation method for enterprise managers according to claim 1, characterized in that: The multi-dimensional standard work data is processed using a preset loss function to construct a linear regression model parameter set for each employee, specifically including: Construct and initialize linear regression model parameters; Based on a preset loss function, the initialized linear regression model parameter set is iteratively updated using a gradient descent algorithm; When the initialized linear regression model parameter set is iterated until the loss function converges, it is used as the linear regression model parameter set to be applied.
3. A work performance evaluation method for enterprise managers according to claim 1, characterized in that: The loss function is expressed by the following formula: θ=(θ0,θ1,…,θ d ) Among them, J(θ) represents the loss function, x i represents the i-th employee in the manager team to be evaluated, h θ (x i ) represents the KPI value of the i-th employee during the iteration process, y i represents the preset KPI target value corresponding to the i-th employee, θ represents the initialized linear regression model parameter set, d represents the dimension of the multidimensional standard work data and the number of model parameters in the initialized linear regression model parameter set, and n represents the number of employees in the manager team to be evaluated.
4. A work performance evaluation method for enterprise managers according to claim 1, characterized in that: Based on the linear regression model parameter set, weighted calculation is performed on the multi-dimensional standard work data of each employee to obtain a first KPI value of each employee, specifically including: combining each model parameter in the linear regression model parameter set with corresponding work data in the multidimensional standard work data to obtain a basic contribution value of the employee on the work task corresponding to the work data; All basic contribution values corresponding to the multi-dimensional standard working data are summed to obtain the first KPI value.
5. A work performance evaluation method for enterprise managers according to claim 1, characterized in that: The method further comprises: Constructing the position-weight mapping relationship specifically includes: Obtaining the job responsibilities of each position within the manager team to be evaluated, and calculating the job weight of the corresponding position based on the job responsibilities using a preset weight calculation logic; The position weights are matched one-to-one with the positions of the employees in the manager team to be evaluated to obtain the position-weight mapping relationship.
6. A work performance evaluation method for enterprise managers according to claim 5, characterized in that: Based on the job responsibilities, the job weight of the corresponding position is calculated through the preset weight calculation logic, specifically including: Decompose the job responsibilities into several tasks and build a hierarchical evaluation system for the tasks based on the enterprise's operational goals; Evaluate the importance of each task item and generate an importance index of each task item in the manager team to be evaluated; Based on the importance indexes of all tasks in each position, determine the sum of the importance indexes of each position; Based on the sum of the importance indexes corresponding to the positions, the position weight of each position in the team of managers to be evaluated is determined.
7. A work performance evaluation method for enterprise managers according to claim 1, characterized in that: The second KPI value is statistically analyzed and visually displayed, specifically including: Performing a time-dimensional comparative analysis on the second KPI value to generate a performance trend report including changes in the second KPI value within a historical period; A dynamic chart is generated based on the performance trend report, and the performance trend report and the dynamic chart are sent to a display device to achieve visual presentation; wherein the dynamic chart includes at least a line chart and / or a heat map and / or a radar chart.
8. A method for evaluating the work performance of enterprise managers according to claim 1, characterized in that: Collect the multi-dimensional original work data of each employee in the management team to be evaluated, and pre-process the multi-dimensional original work data, specifically including: Eliminate outliers from the multidimensional original work data of each employee and fill in missing values; The multidimensional original work data of each employee is standardized by using a z-score standardization method to eliminate the dimension of the multidimensional original work data of each employee and obtain standardized data.
9. A work performance evaluation device for enterprise managers, characterized in that: The device comprises: at least one processor; and, a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a work performance evaluation method for enterprise managers as described in any one of claims 1 to 8.
10. A non-volatile computer storage medium for evaluating the work performance of enterprise managers, storing computer-executable instructions, characterized in that: When the computer-executable instructions are executed, a work performance evaluation method for enterprise managers according to any one of claims 1 to 8 is implemented.