Performance management assessment system based on big data and mathematical model

Abstract

The invention provides a performance management assessment system based on big data and a mathematical model, and the system comprises a data collection module which is used for collecting the work performance data of employees from a plurality of data sources in real time; the data preprocessing module is used for carrying out cleaning, denoising, missing value complementing and normalization processing on the collected original data; the index weight calculation module is used for calculating weights of indexes of different dimensions based on an analytic hierarchy process; the performance scoring module is used for generating comprehensive performance scores of the employees through a fuzzy comprehensive evaluation model; the time sequence prediction module is used for predicting future performance of the employees in combination with historical performance data and a machine learning algorithm; and the dynamic feedback and adjustment module provides personalized feedback and improvement suggestions for the employees based on the real-time performance data. According to the invention, multi-dimensional quantitative evaluation can be carried out on the work performance of the employee, personalized improvement suggestions are provided, and future performance performance of the employee is analyzed through prediction.

Description

Technical Field

[0001] The present invention belongs to the technical field of enterprise management, and in particular relates to a performance management and assessment system based on big data and mathematical models. Background Art

[0002] In modern enterprise management, performance management is an important means to enhance employee enthusiasm, optimize work efficiency, and achieve corporate goals. Traditional performance appraisal methods mainly rely on the subjective judgment of supervisors and a small number of quantitative indicators, which are difficult to fully and objectively reflect the work performance of employees. With the development of information technology, big data and artificial intelligence technologies are gradually applied to enterprise management, providing technical support for the automation and precision of performance appraisal.

[0003] The current performance appraisal system on the market has the following problems:

[0004] Limited assessment data: Mostly based on simple quantitative indicators, lacking comprehensive tracking of employee work behavior.

[0005] Insufficient model complexity: It is difficult to consider the nonlinear changes in employee performance and cannot accurately portray the actual contributions of employees.

[0006] The assessment results are highly subjective: Traditional methods rely heavily on supervisors, and the assessment results are easily influenced by personal subjectivity.

[0007] Therefore, there is an urgent need for a performance management and assessment system based on big data and mathematical models, which can make full use of employees' behavioral data and performance and combine mathematical models to achieve more accurate and scientific performance evaluation. Summary of the invention

[0008] The purpose of the present invention is to overcome the shortcomings of the prior art and propose a performance management and assessment system based on big data and mathematical models. The system integrates employee behavior data, work result data, environmental impact and other factors, uses big data analysis technology and mathematical models to build a scientific and objective performance evaluation system, reduce subjectivity in the assessment, and provide a visual performance analysis report.

[0009] The technical solution of the present invention is as follows:

[0010] A performance management and assessment system based on big data and mathematical models, the system comprising:

[0011] Data collection module, which is responsible for collecting employee performance data from multiple data sources in real time;

[0012] The data preprocessing module cleans, denoises, fills in missing values, and normalizes the collected raw data;

[0013] The indicator weight calculation module calculates the weights of indicators of different dimensions based on the hierarchical analysis method;

[0014] The performance scoring module generates comprehensive performance scores of employees through a fuzzy comprehensive evaluation model;

[0015] A time series forecasting module that combines historical performance data and machine learning algorithms to predict employees’ future performance;

[0016] Furthermore, the employee's work performance data includes work completion, customer satisfaction, innovation ability, teamwork and time management.

[0017] Furthermore, the data preprocessing module includes:

[0018] The data cleaning module uses the mean completion method or K-nearest neighbor algorithm to fill in missing values, remove outliers, and remove incomplete or abnormal data;

[0019] Data denoising module, which filters noise data through statistical analysis and rule detection;

[0020] The data standardization and normalization module uses normalization technology to unify data of different dimensions into a unified range.

[0021] The data conversion module converts qualitative data into structured data through natural language processing technology.

[0022] Furthermore, the performance scoring module includes:

[0023] Indicator weight allocation module, used to assign weights to different assessment indicators;

[0024] The fuzzy comprehensive evaluation module is used to generate the comprehensive performance score of employees through the fuzzy comprehensive evaluation model;

[0025] The abnormal score processing module is used to automatically identify and mark these abnormal values ​​to prevent them from causing unreasonable impact on the overall performance score.

[0026] Furthermore, the fuzzy comprehensive evaluation model formula is:

[0027]

[0028] Among them, F is the fuzzy comprehensive evaluation result, which represents the comprehensive performance score of the employee, and w i is the weight of the i-th indicator, r ij is the membership degree of the i-th employee in the j-th evaluation level.

[0029] Furthermore, the time series prediction module includes:

[0030] Data smoothing module, used to remove noise from performance data;

[0031] The prediction model training and optimization module uses the differential autoregressive moving average model or LSTM long short-term memory network to perform prediction modeling and optimize model parameters through historical data;

[0032] Model output module, used to output future employee performance rating trends.

[0033] Furthermore, the system also includes a dynamic feedback and adjustment module, which provides personalized feedback and improvement suggestions to employees based on real-time performance data.

[0034] Furthermore, the dynamic feedback and adjustment module includes:

[0035] Real-time feedback module, which generates performance scores based on real-time current data and presents employee performance through a visual interface;

[0036] Dynamic goal adjustment module, which automatically sets new goals based on employees’ real-time performance, the team’s overall performance, and the company’s strategic goals;

[0037] Personalized improvement suggestions, combined with employees' historical data, analyze employees' performance weaknesses and provide targeted improvement suggestions

[0038] Compared with the prior art, the present invention has the following advantages:

[0039] Multi-dimensional assessment: Collecting multi-dimensional data through big data technology avoids the single-dimensional assessment problem in traditional performance management.

[0040] Objective evaluation: Use mathematical models to calculate employees' performance scores, reduce the impact of human subjective factors, and ensure the fairness of performance evaluation.

[0041] Real-time feedback and dynamic adjustment: The system can collect data in real time and evaluate employee performance, helping employees to adjust their working methods and goals in a timely manner.

[0042] Future performance prediction: Through historical data and time series analysis, the system can predict employees’ future performance in advance and help companies allocate resources reasonably.

[0043] Personalized improvement suggestions: The system provides customized performance improvement suggestions based on the specific performance of employees, helping employees improve their work efficiency and performance levels. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The accompanying drawings generally illustrate various embodiments by way of example and not limitation, and together with the description and claims, serve to illustrate the embodiments of the invention. Where appropriate, the same reference numerals are used throughout the drawings to refer to the same or similar parts. Such embodiments are illustrative and are not intended to be exhaustive or exclusive embodiments of the present apparatus or method.

[0045] Figure 1 A schematic diagram of the system framework of the present invention is shown. DETAILED DESCRIPTION

[0046] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0047] The performance management and assessment system in this embodiment includes the following modules:

[0048] Data acquisition module

[0049] Obtain data such as employee working hours, task completion, project progress, etc. from the company's ERP system.

[0050] Obtain customer feedback data on employee service quality from the CRM system.

[0051] Obtain employee attendance, leave and other data from the human resources management system.

[0052] Extract text information from employees' work logs and analyze qualitative data such as their work attitude and teamwork ability.

[0053] Data preprocessing module

[0054] For task data in the ERP system, the system first removes invalid data (such as duplicate records, incorrect input, etc.).

[0055] Through natural language processing (NLP) technology, sentiment analysis is performed on the text in the employee's work log to convert qualitative data into quantitative data. For example, if positive words (such as "solved" and "successful") appear many times in an employee's work log, the system will give the employee a higher score for his work attitude.

[0056] Normalization: Since data in different dimensions have different dimensions, the system uses normalization methods to standardize all data to ensure that they are compared within the same range.

[0057] Index weight calculation module

[0058] According to the enterprise's assessment requirements, the system uses the analytic hierarchy process (AHP) to assign weights to assessment indicators of different dimensions.

[0059] Based on the hierarchical analysis method, the weight formula for calculating indicators of different dimensions is:

[0060]

[0061] Among them, w i is the weight of the ith indicator, a i Rate the relative importance of this indicator

[0062] For example, for employees in the R&D department, the system assigns a higher weight to “technical ability”, while for employees in the marketing department, it assigns a higher weight to “customer satisfaction”.

[0063] The specific implementation steps of the hierarchical analysis method include:

[0064] Build a hierarchical structure: decompose the problem into different levels, usually including the goal level, the criterion level and the indicator level. Taking this system as an example, the goal level is "employee performance appraisal", the criterion level includes "technical ability", "work attitude", "teamwork", etc., and the indicator level is the specific indicators under each criterion.

[0065] Construct a pairwise comparison matrix: Based on experience or actual business needs, managers conduct pairwise comparisons of different dimensions to determine the importance of each dimension. Each element of the comparison matrix represents the importance of one dimension relative to another dimension.

[0066] Calculate the weight vector: According to the pairwise comparison matrix, use the eigenvector method or geometric mean method to calculate the weight vector of each dimension. The larger the weight vector, the more important the dimension is in the overall evaluation.

[0067] Consistency check: In order to ensure the rationality of the judgment matrix, a consistency check is required. If the consistency ratio (CR) is less than 0.1, the judgment results of the matrix are consistent and the weight distribution is reasonable.

[0068] The present invention takes into account the flexibility of enterprise performance appraisal in its design. In different appraisal cycles, enterprises may adjust the focus of performance appraisal. For example, in the R&D department, one quarter may focus more on innovation ability, while the next quarter may focus more on project delivery. Therefore, the system allows managers to dynamically adjust the weight of indicators according to actual business needs.

[0069] In actual operation, the system will automatically recommend weight adjustment suggestions based on historical data and feedback from managers. Managers can choose to accept the system's suggestions or adjust the weights manually.

[0070] Performance Rating Module

[0071] Employees are evaluated through fuzzy mathematics methods.

[0072] The basic steps of the fuzzy comprehensive evaluation model are as follows:

[0073] Determine the evaluation index set: Assume that there are n assessment dimensions, forming the evaluation index set U = {U1, U2, ..., U n};

[0074] Determine the weight set: The weight of each assessment dimension constitutes the weight set W = {w1,w2…,w n}, the weight set is calculated by methods such as AHP or PCA.

[0075] Fuzzy evaluation matrix: Construct a fuzzy evaluation matrix based on employee performance in It indicates the employee's membership degree to the jth evaluation level on the i-th dimension.

[0076] Fuzzy comprehensive evaluation: The final fuzzy comprehensive evaluation result is calculated by the following formula:

[0077]

[0078] Among them, F is the fuzzy comprehensive evaluation result, which represents the comprehensive performance score of the employee, and w i is the weight of the i-th indicator, r ij is the membership degree of the i-th employee in the j-th evaluation level.

[0079] Construction of fuzzy evaluation matrix in fuzzy comprehensive evaluation:

[0080]

[0081] During the scoring process, extreme or abnormal scores may appear (for example, scores on certain dimensions are much higher or lower than expected). The abnormal score processing module is used to automatically identify and mark these abnormal values ​​to prevent them from having an unreasonable impact on the overall performance score. Processing methods include data cleaning, weighted correction, etc.

[0082] The system can also dynamically adjust the weight of each dimension according to the needs of the enterprise. For example, in the annual assessment, the enterprise can increase the weight of "teamwork ability" to encourage employees to enhance their teamwork spirit.

[0083] Time Series Forecasting Module

[0084] The system performs time series analysis on employees’ historical performance data to predict their future performance.

[0085] The models used include ARIMA (Autoregressive Integrated Moving Average) and LSTM (Long Short-Term Memory), which can effectively process time series data and predict future performance trends.

[0086] The ARIMA model is used to predict employees' future performance scores. First, the system performs a difference operation on the employee's historical performance data to make it stable. Then, the least squares method is used to estimate the autoregressive coefficient and the moving average coefficient, and finally the ARIMA model is used to predict the employee's future performance trend.

[0087] For example, if an employee's performance ratings have been relatively stable over the past few quarters, the system can predict that his or her future performance will remain at a high level; for employees whose performance fluctuates greatly, the system can predict the uncertainty of their future performance and provide corresponding early warning information.

[0088] Dynamic feedback and adjustment module

[0089] The system generates feedback reports based on employees’ real-time performance and provides personalized suggestions for improvement. For example, if an employee’s “teamwork ability” score is low, the system will recommend that he or she participate in relevant training courses to improve teamwork skills.

[0090] The system also allows employees and management to set new performance goals and dynamically adjust goals based on real-time data to ensure that goals are achievable.

[0091] All feedback information is presented in a visual way, and employees can view their performance scores, improvement suggestions and future performance forecasts at any time.

[0092] The present invention can not only comprehensively evaluate the current performance of employees, but also provide enterprises with predictions and early warnings of future performance through time series prediction models, helping management to make more scientific decisions. At the same time, the system's real-time feedback and personalized improvement suggestion functions can help employees adjust their working methods in a timely manner in their daily work, thereby improving the overall performance level.

[0093] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A performance management and assessment system based on big data and mathematical models, characterized in that: The system comprises: Data collection module, which is responsible for collecting employee performance data from multiple data sources in real time; The data preprocessing module cleans, denoises, fills in missing values, and normalizes the collected raw data; The indicator weight calculation module calculates the weights of indicators of different dimensions based on the hierarchical analysis method; The performance scoring module generates comprehensive performance scores of employees through a fuzzy comprehensive evaluation model; The time series forecasting module combines historical performance data and machine learning algorithms to predict employees' future performance.

2. The performance management and assessment system based on big data and mathematical models according to claim 1 is characterized in that: The employee's work performance data includes work completion, customer satisfaction, innovation ability, teamwork and time management.

3. The performance management and assessment system based on big data and mathematical models according to claim 1 is characterized in that: The formula for calculating the weights of indicators of different dimensions based on the hierarchical analysis method is as follows: Among them, w i is the weight of the ith indicator, a i Rate the relative importance of this indicator.

4. The performance management and assessment system based on big data and mathematical models according to claim 1 is characterized in that: The data preprocessing module comprises: The data cleaning module uses the mean completion method or K-nearest neighbor algorithm to fill in missing values, remove outliers, and remove incomplete or abnormal data; Data denoising module, which filters noise data through statistical analysis and rule detection; Data standardization and normalization module: Use normalization technology to unify data of different dimensions into a unified range. The data conversion module converts qualitative data into structured data through natural language processing technology.

5. The performance management and assessment system based on big data and mathematical models according to claim 1 is characterized in that: The performance scoring module includes: Indicator weight allocation module, used to assign weights to different assessment indicators; The fuzzy comprehensive evaluation module is used to generate the comprehensive performance score of employees through the fuzzy comprehensive evaluation model; The abnormal score processing module is used to automatically identify and mark these abnormal values ​​to prevent them from causing unreasonable impact on the overall performance score.

6. The performance management and assessment system based on big data and mathematical models according to claim 1 is characterized in that: The fuzzy comprehensive evaluation model formula is: Among them, F is the fuzzy comprehensive evaluation result, which represents the comprehensive performance score of the employee, and w i is the weight of the i-th indicator, r ij is the membership degree of the i-th employee in the j-th evaluation level.

7. The performance management and assessment system based on big data and mathematical models according to claim 1 is characterized in that: The time series prediction module includes: Data smoothing module, used to remove noise from performance data; The prediction model training and optimization module uses the differential autoregressive moving average model or LSTM long short-term memory network to perform prediction modeling and optimize model parameters through historical data; Model output module, used to output future employee performance rating trends.

8. The performance management and assessment system based on big data and mathematical models according to claim 1 is characterized in that: The system also includes a dynamic feedback and adjustment module that provides employees with personalized feedback and improvement suggestions based on real-time performance data.

9. The performance management and assessment system based on big data and mathematical models according to claim 7 is characterized in that: The dynamic feedback and adjustment module includes: Real-time feedback module, which generates performance scores based on real-time current data and presents employee performance through a visual interface; Dynamic goal adjustment module, which automatically sets new goals based on employees’ real-time performance, the team’s overall performance, and the company’s strategic goals; Personalized improvement suggestions, combined with employees' historical data, analyze employees' performance weaknesses and provide targeted improvement suggestions.

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