Performance assessment system for human resources

Abstract

The invention discloses a human resource performance appraisal system, and relates to the technical field of performance appraisal, and the system comprises an information collection module which is responsible for collecting performance data of employees in a multi-source manner; the data processing module is used for automatically processing the collected data; the analysis and evaluation module generates a performance evaluation score based on the performance data; the performance prediction module predicts future performance trends of the employees by constructing a model; and the optimization adjustment module divides performance levels according to evaluation and prediction results. The processing efficiency of the performance assessment system can be improved by collecting various performance index data and automatically processing the collected data, the accuracy of the performance score result can be improved by eliminating unreasonable data in the sample data, and the accuracy of the performance score result can be improved by predicting the subsequent performance score. And corresponding performance optimization suggestions are given according to a prediction result and an actual result, so that subsequent performance index optimization is facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of performance appraisal, and in particular to a human resources performance appraisal system and method. Background Art

[0002] A performance appraisal system is a management tool used by organizations to systematically evaluate employee performance. It aims to optimize both organizational effectiveness and employee development. Through goal decomposition and multi-dimensional assessment, it transforms corporate strategy into actionable individual performance indicators. Its role is primarily reflected in three key areas: first, establishing a scientific assessment system that leverages behavioral analysis tools to objectively measure employee contributions and capabilities. Second, it enables data-driven management decisions, relying on big data analysis to predict performance trends, identify high-potential talent, and optimize resource allocation. Third, it promotes the collaborative development of the organization and its employees. Through real-time feedback, personalized training recommendations, and dynamic goal adjustments, it enhances employee competency while driving organizational process optimization and strategic iteration.

[0003] In the context of human resources digital transformation, traditional performance appraisal systems face core flaws such as data silos, rigid evaluation models, and insufficient real-time performance. Limited by a single architecture and unstructured data processing capabilities, existing solutions struggle to meet the demands of dynamic business scenarios. With the maturity of technologies like AI, federated learning, and edge computing, existing technologies have implemented basic management functions for enterprise performance management, including displaying company performance data, managing KPIs, monitoring the progress of key projects at the company and department levels, and setting indicators for organizational performance growth. However, these technologies lack intelligent analysis of performance growth factors. For core tasks like key projects, they focus solely on goal setting and progress display, lacking suggestions for optimizing execution strategies, and thus have not formed a complete performance appraisal system. Summary of the Invention

[0004] The present invention provides a human resources performance appraisal system and method to solve the technical problems of insufficient integrity of performance appraisal data, lack of analysis during the appraisal process, and weak feedback optimization management in existing enterprise performance appraisals.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] The present invention provides a human resources performance appraisal system, comprising:

[0007] Information collection module, data processing module, analysis and evaluation module, performance prediction module, optimization and adjustment module;

[0008] The information collection module is responsible for collecting employee performance data from multiple sources and supports periodic data updates to ensure the stability of the collected data;

[0009] The data processing module is used to automatically process the collected data to provide a highly reliable normalized data set;

[0010] The analysis and evaluation module generates performance evaluation scores based on performance data and supports dynamic adjustment of assessment indicator weights;

[0011] The performance prediction module predicts employees' future performance trends by building models, assisting in the adjustment and improvement of subsequent performance plans;

[0012] The optimization and adjustment module divides performance levels according to the evaluation and prediction results, and generates corresponding prompt information based on the performance levels.

[0013] The beneficial effects brought about by the technical solution provided by the present invention include at least:

[0014] The present invention collects a variety of performance indicator data through an information collection module and automatically processes the collected data through a data processing module, thereby improving the processing efficiency of the performance appraisal system.

[0015] The present invention can calculate the performance score based on the performance data through the analysis and evaluation module, and can improve the accuracy of the performance score result by eliminating unreasonable data in the sample data.

[0016] The present invention can predict subsequent performance scores through the performance prediction module, and the optimization adjustment module gives corresponding performance optimization suggestions based on the prediction results and actual results, which facilitates the optimization of subsequent performance indicators. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 This is a flow chart of a human resources performance appraisal system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] To make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0020] This embodiment provides a human resources performance appraisal system and method.

[0021] 1. Information Collection Module

[0022] The data collected by the information collection module includes KPI indicator data and project progress data. The KPI indicator data is connected to the financial system and acquired periodically. The project progress data is updated in real time through the project API.

[0023] The KPI indicator data includes individual KPI indicators and organizational KPI indicators. The individual KPI indicators include business completion rate and personal behavior performance. The personal behavior performance includes ability assessment, business contribution, and development score. The organizational KPI indicators include marketing rate, profit margin, and growth rate.

[0024] It should be noted that the performance of capability assessment data is calculated based on personnel effectiveness. The proportion of personnel effectiveness for different positions can be adjusted appropriately. For example, the violation rate for production positions accounts for 70%, and the working hours for customer service positions account for 70%. Business contribution and development scores can be obtained through personal evaluation of employees by department heads or direct leaders.

[0025] The project progress data includes project completion rate, project deviation rate, and resource input rate, and the resource input rate is obtained by human resources input, financial input, and equipment input.

[0026] It should be noted that the project deviation rate is calculated by the ratio of actual progress to planned progress. The smaller the ratio deviation, the smaller the deviation rate. For example, if the actual progress differs from the planned progress by three days, the ratio is 0.3.

[0027] 2. Data Processing Module

[0028] The data processing module includes a data integration unit, a quality monitoring unit, and a security management unit. The data integration unit is responsible for achieving standardized processing of performance data, the quality monitoring unit is responsible for continuous monitoring and verification of data, and the security management unit is responsible for protecting sensitive performance data from tampering and leakage.

[0029] The data are standardized by using Z-score to unify the data magnitude;

[0030] The quality control includes data verification and data repair. The data verification divides the interval threshold of abnormal data by CHECK constraints, and the data is supplemented by filling the default value or average value to supplement the missing data.

[0031] It should be noted that the supplement of default values ​​and average values ​​requires sending a confirmation message to the administrator, and the average value is given priority. If the difference between the average value and the associated data is too large, the default value is used.

[0032] The security management includes data encryption and setting an anti-tampering mechanism. The data uses AES encryption content and is transmitted through a secure channel. The anti-tampering mechanism is implemented by generating a SHA-256 hash value.

[0033] It should be noted that data types can be divided into highly sensitive data such as performance scores and performance completion amounts, medium-sensitive data such as positions and salaries, and low-sensitive data such as names and department names during data security management. By setting different security protection mechanisms for different data, data transmission overload can be avoided.

[0034] 3. Analysis and Evaluation Module

[0035] The analysis and evaluation module evaluates the performance indicator data to determine whether the performance indicator data is qualified. The performance indicator data evaluation completes the ranking of the performance indicator data by setting an ideal value, as shown in the following formula:

[0036]

[0037]

[0038]

[0039]

[0040]

[0041] Where, Indicates the performance indicator data evaluation results; Indicates the negative ideal value of the performance indicator data; Indicates the positive ideal value of the performance indicator data; represents the weight factor of the jth performance indicator; Represents the data value of the i-th sample at the j-th performance indicator; It represents the minimum ideal value of the performance indicator; It represents the maximum ideal value of the performance indicator;

[0042] After the performance indicator data are sorted, dimensionless sample processing is used to convert the collected sample data into performance evaluation values, as shown in the following formula:

[0043]

[0044] Where, Represents the performance evaluation value after sample data transformation; Indicates the score value of the sample data;

[0045] It should be noted that five levels are selected here to define the evaluation value of the sample. The five levels are the same as the performance recommendation levels. The indicators are divided by the accuracy of qualitative indicators using the semantic difference membership assignment method, which can reduce the occurrence of subjective evaluation errors.

[0046] After the sample data is converted, the performance data is evaluated by constructing a performance appraisal model, as shown in the following formula:

[0047]

[0048] Where, Indicates the evaluation value of the performance indicator; express The weight factor.

[0049] During the sample data conversion process, the rationality of the sample data is judged, and the sample data is processed by the maximum and minimum values ​​of the sample data to reduce the error in the sample data, as shown in the following formula:

[0050]

[0051]

[0052]

[0053] Where, Represents the standardized data value; represents the actual value of the sample; Indicates the maximum value of the sample data; Indicates the minimum value of the sample data.

[0054] 5. Performance Prediction Module

[0055] The performance prediction module predicts the subsequent performance score by constructing the Holt-Winters model and achieves accurate prediction by introducing a double smoothing parameter optimization model, as shown in the following formula:

[0056]

[0057]

[0058]

[0059] Where, represents the actual performance evaluation score; represents the predicted performance evaluation score; represents the local growth coefficient; Indicates the number of periods for which performance results are adopted; 、 Represents the smoothing coefficient, with a value between (0,1);

[0060] The Holt-Winters model adjusts the model parameters by introducing seasonal factors, so that the model can fully analyze the data characteristics, as shown in the following formula:

[0061]

[0062]

[0063]

[0064] Where: represents seasonal influence parameter; Indicates the change trend parameter; Represents the prediction error value; Indicates the impact parameter of the previous quarter; Represents seasonal cycles; represents the smoothing parameter of the forecast error.

[0065] It should be noted that for parameters with less influence from seasonal factors, , in order to reduce the impact of seasonal factors on performance forecast results.

[0066] 6. Optimization and Adjustment Module

[0067] The optimization and adjustment module includes a level division unit and an information prompt unit. The level division unit is used to divide the performance indicator level according to the user's actual performance evaluation score and predicted score. The information prompt unit sends different prompt information to the user according to different performance levels.

[0068] The performance level is divided into five levels: excellent, good, qualified, needs improvement, and unqualified. The standard for the excellent level is 85 points or above, the standard for the good level is 70-84 points, the standard for the qualified level is 60-69 points, the standard for the needs improvement level is 50-59 points, and the standard for the unqualified level is 49 points or below;

[0069] It should be noted that the division of performance levels needs to be based on the average of the performance rating results of three cycles, and the ratio between the actual score and the predicted score is 1:1. The ratio can be adaptively adjusted for different positions. For example, the ratio between the actual score and the predicted score for a sales position is 0.7:0.3.

[0070] At the excellent level, public information is sent to other users to display the actual performance score. At the good level, actual performance forecast score information is sent to individuals, and comparison details with excellent level performance data are displayed. At the qualified level, forecast performance score details are sent to individuals, and users can make adaptive adjustments based on the forecast performance score. At the level for improvement, forecast performance scores are sent to individual users and early warning information is provided. At the unqualified level, actual and forecast performance scores are sent and a prompt message is sent to the administrator.

[0071] It should be noted that the purpose of sending the actual predicted score is to help users understand their own performance, and the purpose of sending the predicted score is to help users understand the direction of performance improvement. Users can verify the results of the performance prediction based on the predicted and actual performance scores.

[0072] A human resources performance appraisal method includes the following steps:

[0073] The information collection module collects multi-source performance appraisal information of employees; the data processing module integrates and standardizes the collected information; the analysis and evaluation module comprehensively analyzes performance indicator data and conducts evaluation; the performance prediction module predicts performance scores based on the evaluation results and historical performance; the optimization and adjustment module classifies performance scores into levels based on actual performance scores and predicted scores, and provides performance optimization tips based on the corresponding levels.

[0074] Furthermore, it should be noted that the present invention may be provided as a method, apparatus, or computer program product. Thus, embodiments of the present invention may take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of the present invention may take the form of a computer program product embodied on one or more computer-usable storage media containing computer-usable program code.

[0075] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks 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, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the process in the flowchart and / or block diagram. 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.

[0076] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device, so that a series of operation steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for implementing the process in 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.

[0077] It should also be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. The terms "comprises," "includes," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or terminal device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or terminal device. In the absence of further restrictions, an element defined by the phrase "comprises a ..." does not exclude the presence of additional identical elements in the process, method, article, or terminal device that includes the element.

[0078] Finally, it should be noted that the above is a preferred embodiment of the present invention. It should be noted that although the preferred embodiment of the present invention has been described, it is clear that those skilled in the art, once they understand the basic inventive concept of the present invention, can make various improvements and modifications without departing from the principles of the present invention. Such improvements and modifications should also be considered as within the scope of protection of the present invention. Therefore, the appended claims are intended to be interpreted as including the preferred embodiment and all changes and modifications that fall within the scope of the embodiments of the present invention.

Claims

1. A human resources performance appraisal system, characterized in that: include: Information collection module, data processing module, analysis and evaluation module, performance prediction module, optimization and adjustment module; The information collection module is responsible for collecting employee performance data from multiple sources and supports periodic data updates to ensure the stability of the collected data; The data processing module is used to automatically process the collected data to provide a highly reliable normalized data set; The analysis and evaluation module generates performance evaluation scores based on performance data and supports dynamic adjustment of assessment indicator weights; The performance prediction module predicts employees' future performance trends by building models, assisting in the adjustment and improvement of subsequent performance plans; The optimization and adjustment module divides performance levels according to the evaluation and prediction results, and generates corresponding prompt information based on the performance levels.

2. The human resources performance appraisal system according to claim 1, characterized in that: The information collection module is responsible for collecting employee performance data from multiple sources and supports periodic data updates to ensure the stability of the collected data. The data collected by the information collection module includes KPI indicator data and project progress data. The KPI indicator data is connected to the financial system and acquired periodically. The project progress data is updated in real time through the project API. The KPI indicator data includes individual KPI indicators and organizational KPI indicators. The individual KPI indicators include business completion rate and personal behavior performance. The personal behavior performance includes ability assessment, business contribution, and development score. The organizational KPI indicators include marketing rate, profit margin, and growth rate. The project progress data includes project completion rate, project deviation rate, and resource input rate, and the resource input rate is obtained by human resources input, financial input, and equipment input.

3. The human resources performance appraisal system according to claim 1, characterized in that: The data processing module is used to automatically process the collected data to provide a highly reliable normalized data set, wherein: The data processing module includes a data integration unit, a quality monitoring unit, and a security management unit. The data integration unit is responsible for achieving standardized processing of performance data, the quality monitoring unit is responsible for continuous monitoring and verification of data, and the security management unit is responsible for protecting sensitive performance data from tampering and leakage. The data are standardized by using Z-score to unify the data magnitude; The quality control includes data verification and data repair. The data verification divides the interval threshold of abnormal data by CHECK constraints, and the data is supplemented by filling the default value or average value to supplement the missing data. The security management includes data encryption and setting an anti-tampering mechanism. The data uses AES encryption content and is transmitted through a secure channel. The anti-tampering mechanism is implemented by generating a SHA-256 hash value.

4. The human resources performance appraisal system according to claim 1, characterized in that: The analysis and evaluation module generates performance evaluation scores based on performance data and supports dynamic adjustment of assessment indicator weights, where: The analysis and evaluation module evaluates the performance indicator data to determine whether the performance indicator data is qualified. The performance indicator data evaluation completes the ranking of the performance indicator data by setting an ideal value, as shown in the following formula: Where, Indicates the performance indicator data evaluation results; Indicates the negative ideal value of the performance indicator data; Indicates the positive ideal value of the performance indicator data; represents the weight factor of the jth performance indicator; Represents the data value of the i-th sample at the j-th performance indicator; It represents the minimum ideal value of the performance indicator; It represents the maximum ideal value of the performance indicator; After the performance indicator data are sorted, dimensionless sample processing is used to convert the collected sample data into performance evaluation values, as shown in the following formula: Where, Represents the performance evaluation value after sample data transformation; Indicates the score value of the sample data; After the sample data is converted, the performance data is evaluated by constructing a performance appraisal model, as shown in the following formula: Where, Indicates the evaluation value of the performance indicator; express The weight factor.

5. After the ranking of the performance indicators as described in claim 4 is completed, dimensionless sample processing is used to convert the collected sample data into performance evaluation values, wherein: During the sample data conversion process, the rationality of the sample data is judged, and the sample data is processed by the maximum and minimum values ​​of the sample data to reduce the error in the sample data, as shown in the following formula: Where, Represents the standardized data value; represents the actual value of the sample; Indicates the maximum value of the sample data; Indicates the minimum value of the sample data.

6. The human resources performance appraisal system according to claim 1, characterized in that: The performance prediction module predicts employees' future performance trends by building a model to assist in the adjustment and improvement of subsequent performance plans, including: The performance prediction module predicts the subsequent performance score by constructing the Holt-Winters model and achieves accurate prediction by introducing a double smoothing parameter optimization model, as shown in the following formula: Where, represents the actual performance evaluation score; represents the predicted performance evaluation score; represents the local growth coefficient; Indicates the number of periods for which performance results are adopted; 、 Represents the smoothing coefficient, with a value between (0,1); The Holt-Winters model adjusts the model parameters by introducing seasonal factors, so that the model can fully analyze the data characteristics, as shown in the following formula: Where: represents seasonal influence parameter; Indicates the change trend parameter; Represents the prediction error value; Indicates the impact parameter of the previous quarter; Represents seasonal cycles; represents the smoothing parameter of the forecast error.

7. The human resources performance appraisal system according to claim 1, characterized in that: The optimization and adjustment module divides the performance levels according to the evaluation and prediction results, and generates corresponding prompt information according to the performance levels, wherein: The optimization and adjustment module includes a level division unit and an information prompt unit. The level division unit is used to divide the performance indicator level according to the user's actual performance evaluation score and predicted score. The information prompt unit sends different prompt information to the user according to different performance levels. The performance level is divided into five levels: excellent, good, qualified, needs improvement, and unqualified. The standard for the excellent level is 85 points or above, the standard for the good level is 70-84 points, the standard for the qualified level is 60-69 points, the standard for the needs improvement level is 50-59 points, and the standard for the unqualified level is 49 points or below; At the excellent level, public information is sent to other users to display the actual performance score. At the good level, actual performance forecast score information is sent to individuals, and comparison details with excellent level performance data are displayed. At the qualified level, forecast performance score details are sent to individuals, and users can make adaptive adjustments based on the forecast performance score. At the level for improvement, forecast performance scores are sent to individual users and early warning information is provided. At the unqualified level, actual and forecast performance scores are sent and a prompt message is sent to the administrator.

8. A human resources performance appraisal method, applied to the human resources performance appraisal system according to any one of claims 1 to 7, characterized in that: The following steps are involved: The information collection module collects multi-source performance appraisal information of employees; the data processing module integrates and standardizes the collected information; The analysis and evaluation module comprehensively analyzes performance indicator data and conducts evaluation; the performance prediction module predicts performance scores based on the evaluation results and historical performance; The optimization and adjustment module divides performance into levels according to actual performance scores and predicted scores, and gives performance optimization tips based on the corresponding levels.

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