Enterprise digital performance quantitative management platform based on machine learning

Abstract

The invention relates to the technical field of enterprise management, in particular to an enterprise digital performance quantitative management platform based on machine learning, which comprises a data acquisition module, a data preprocessing module, a feature extraction module, an evaluation model construction module, a performance evaluation module, a decision support module and a visual display module. The data acquisition module acquires multi-dimensional data in real time, the data preprocessing module preprocesses the multi-dimensional data, the feature extraction module extracts features of the multi-dimensional data, the evaluation model construction module constructs an enterprise performance evaluation model based on the extracted features, and the performance evaluation module predicts and evaluates the performance of employees or departments in real time. The decision support module provides scientific decision support based on the evaluation result, and the visual display module displays the multi-dimensional data, the performance evaluation result and the management decision through a visual interface; according to the invention, efficiency and precision of enterprise performance management can be greatly improved, resource configuration is optimized, and strategic goal realization of enterprises is promoted.

Description

Technical Field

[0001] The present invention relates to the field of enterprise management technology, and in particular to an enterprise digital performance quantification management platform based on machine learning. Background Art

[0002] With the development of the economy and the intensification of market competition, corporate performance management has become an important tool for measuring corporate operational efficiency and improving competitiveness. Performance evaluation is not only about assessing employee performance, but also about evaluating the operating conditions of departments, projects, and the entire enterprise. In the context of digital transformation, enterprises are faced with a large amount of complex performance data, including employee personal performance data, department work efficiency, financial data, market competitiveness, customer satisfaction, and other multi-dimensional information.

[0003] At present, traditional performance evaluation methods rely on manual operation, empirical judgment and analysis based on historical data, which often lack real-time, accuracy and systematicness. Many companies still rely on manual summary, chart analysis and other methods, which makes it difficult to comprehensively and dynamically process and analyze complex performance data. At the same time, traditional performance evaluation methods lack in-depth exploration of the relationship between corporate strategic goals and resource allocation, and cannot quickly adjust resource allocation based on real-time performance data, affecting the timeliness and accuracy of decision-making. Summary of the invention

[0004] The present invention provides an enterprise digital performance quantification management platform based on machine learning.

[0005] The enterprise digital performance quantitative management platform based on machine learning includes data collection module, data preprocessing module, feature extraction module, evaluation model construction module, performance evaluation module, decision support module and visualization display module, among which;

[0006] The data collection module is used to collect multi-dimensional data related to enterprise performance from various business systems and external data sources of the enterprise in real time, and the multi-dimensional data includes individual employee performance data, department performance data, financial data, market data and customer satisfaction data;

[0007] The data preprocessing module is used to preprocess the collected multi-dimensional data, preprocess data cleaning, data standardization and outlier detection;

[0008] The feature extraction module is used to extract features that have an impact on enterprise performance evaluation from the pre-processed multi-dimensional data;

[0009] The evaluation model building module builds an enterprise performance evaluation model based on the extracted features;

[0010] The performance evaluation module predicts and evaluates the performance of employees or departments in real time through the enterprise performance evaluation model;

[0011] The decision support module provides management decision support based on the output of the performance forecasting and evaluation module and in combination with the enterprise's strategic goals and resource allocation requirements;

[0012] The visualization display module displays multi-dimensional data, performance evaluation results and management decisions through a visualization interface.

[0013] Optionally, the data acquisition module includes:

[0014] Data source interface design: The data collection module is connected with various business systems within the enterprise and external data sources through a unified interface;

[0015] Multi-dimensional data collection: The data collection module collects multi-dimensional data related to enterprise performance through the data interface;

[0016] Real-time data update and synchronization: The data collection module sets the collection frequency according to the characteristics of different data sources.

[0017] Optionally, the data preprocessing module includes:

[0018] Data cleaning: Clean the collected multi-dimensional data to remove missing values ​​in the data;

[0019] Data standardization: The Z-Score standardization method is used to standardize the collected multi-dimensional data and unify the data into the same dimension;

[0020] Outlier detection: Perform outlier detection on the collected multi-dimensional data, identify abnormal points in the data, and process them.

[0021] Optionally, the feature extraction module includes:

[0022] Feature extraction: The feature extraction module extracts features from the preprocessed multi-dimensional data. The extracted features include individual employee performance features, department performance features, financial data features, market and competitiveness features, and customer satisfaction features.

[0023] Feature construction: The feature extraction module enhances the expressiveness of the data by constructing new features, including interactive features and macro features;

[0024] Feature screening: The feature extraction module uses L1 regularization to screen features. L1 regularization can make the weights of some features approach zero through penalty terms, thereby automatically selecting the features that contribute most to performance evaluation.

[0025] Optionally, the evaluation model building module specifically includes:

[0026] Model construction: Construct an enterprise performance evaluation model based on the gradient boosting decision tree algorithm;

[0027] Model training: training the constructed enterprise performance evaluation model;

[0028] Model evaluation: After training is completed, the performance of the enterprise performance evaluation model is evaluated.

[0029] Model application: The enterprise performance evaluation model after training and evaluation is deployed in the performance quantification management platform for real-time prediction of enterprise performance.

[0030] Optionally, the performance evaluation module includes:

[0031] Model prediction: The performance evaluation module inputs real-time multi-dimensional data into the enterprise performance evaluation model, and the model outputs the predicted employee or department performance score;

[0032] Performance evaluation: Based on the prediction results output by the model, the performance evaluation module compares the predicted values ​​with the company's performance standards to evaluate the performance of employees or departments.

[0033] Optionally, the decision support module includes:

[0034] Obtaining performance forecast and evaluation results: The decision support module obtains the output of the performance evaluation module in real time, that is, the performance forecast results of employees or departments;

[0035] Integrate enterprise strategic goals and resource allocation needs: The decision support module combines the enterprise's strategic goals and resource allocation needs to ensure that decisions are consistent with the enterprise's development goals;

[0036] Decision reasoning based on rule engine: The decision support module uses the rule engine to infer management decisions based on performance evaluation results and corporate strategic goals;

[0037] Decision result output: The decision support module gives specific management decision recommendations based on current performance evaluation results, strategic goals and resource requirements.

[0038] Optionally, the visual display module includes:

[0039] Data display interface design: The visualization module provides a multi-dimensional data display interface, which allows users to clearly and intuitively view multi-dimensional data related to corporate performance;

[0040] Visualization of performance evaluation results: The evaluation results provided by the performance evaluation module are displayed through a visual interface;

[0041] Management decision visualization: The visualization display module constructs decision support charts based on the management decisions provided by the decision support module;

[0042] Interactive interface: The visualization module provides users with interactive functions between data.

[0043] Optionally, the visualization module also provides a report generation function, supporting users to export performance evaluation results and management decisions into reports in multiple formats (including PDF and Excel).

[0044] Beneficial effects of the present invention:

[0045] The present invention, through a performance evaluation model based on machine learning, combined with various business systems and external data sources of the enterprise, can collect, process and evaluate the multi-dimensional performance data of the enterprise in real time. The data acquisition module obtains the key performance indicators of employees, departments and the enterprise as a whole from multiple data sources such as the ERP system, human resource management system, and financial management system in real time. Combining the automated processing and feature extraction of multi-dimensional data, the enterprise performance evaluation model is constructed using the gradient boosting decision tree algorithm, which can more accurately evaluate the performance of employees and departments. This process avoids the errors and timeliness problems of relying on manual operations in traditional evaluation methods, and realizes real-time prediction and dynamic monitoring.

[0046] The present invention provides scientific and reasonable decision-making suggestions based on the performance evaluation results by combining the strategic goals and resource allocation needs of the enterprise. The decision support module provides managers with optimized resource allocation solutions based on the output of the enterprise performance evaluation model and the actual needs of the enterprise. By real-time monitoring of the performance of various departments and employees, managers can quickly discover imbalances and potential problems in resource allocation, make timely adjustments, further improve the efficiency of enterprise resource utilization, and promote the realization of strategic goals.

[0047] In the present invention, the visualization display module converts multi-dimensional data and performance evaluation results into intuitive and easy-to-understand graphics and charts through a visualization interface, helping managers to quickly understand the current operating status of the enterprise. Whether it is individual employee performance or department-level performance evaluation, it can be presented in various forms such as charts or heat maps, allowing managers to view the real-time status and historical trends of enterprise operations at different levels. At the same time, the visualization of management decisions makes the decision-making process more transparent, enhances information communication and feedback between managers and employees, and improves the overall operating efficiency and decision-making quality of the enterprise. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0049] Figure 1 A schematic diagram of a platform flow chart of an embodiment of the present invention;

[0050] Figure 2 A schematic diagram of a module flow chart of an evaluation model construction according to an embodiment of the present invention;

[0051] Figure 3 The figure is a flowchart of a decision support module according to an embodiment of the present invention. DETAILED DESCRIPTION

[0052] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0053] It should be noted that the references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0054] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0055] like Figure 1-Figure 3 As shown, the enterprise digital performance quantification management platform based on machine learning includes data acquisition module, data preprocessing module, feature extraction module, evaluation model construction module, performance evaluation module, decision support module and visualization display module, among which;

[0056] The data collection module is used to collect multi-dimensional data related to enterprise performance from various business systems and external data sources in real time. The multi-dimensional data includes individual employee performance data, department performance data, financial data, market data and customer satisfaction data;

[0057] The data preprocessing module is used to preprocess the collected multi-dimensional data, including preprocessing data cleaning, data standardization and outlier detection;

[0058] The feature extraction module is used to extract features that have an impact on enterprise performance evaluation from preprocessed multi-dimensional data;

[0059] The evaluation model building module builds an enterprise performance evaluation model based on the extracted features;

[0060] The performance evaluation module uses the enterprise performance evaluation model to predict and evaluate the performance of employees or departments in real time;

[0061] The decision support module provides management decision support based on the output of the performance forecasting and evaluation module and in combination with the enterprise's strategic goals and resource allocation needs;

[0062] The visualization module displays multi-dimensional data, performance evaluation results and management decisions through a visualization interface.

[0063] The data acquisition module includes:

[0064] Data source interface design: The data acquisition module is connected to various business systems and external data sources within the enterprise through a unified interface. Each data source uses a standardized interface to support scheduled or real-time data transmission. The interface design is customized according to the format and protocol of different data sources to ensure that data can be effectively acquired.

[0065] Various business systems within the enterprise include HR system, ERP system, human resource management system, and financial management system;

[0066] External data sources include external market analysis platforms and social media monitoring platforms;

[0067] Multi-dimensional data collection: The data collection module collects multi-dimensional data related to enterprise performance through the data interface, including:

[0068] Employee individual performance data: obtain information such as employee task completion rate, performance appraisal score, work quality, etc. through the human resources management system, and use the API interface to obtain employee work records from the HR system regularly and synchronize them;

[0069] Department performance data: Obtain department performance data through ERP system, financial management system, production management system, etc., including production efficiency, sales, project success rate, etc. These data are connected with the interfaces of each system, updated and synchronized daily;

[0070] Financial data: collect financial data such as income, expenditure, profit, cost, etc. in real time through the company's financial management system;

[0071] Market data: Obtain market data from external market analysis platforms to ensure that the collected data covers the competition landscape and market trends;

[0072] Customer satisfaction data: Collect customer satisfaction data through social media monitoring platforms, online questionnaires or customer feedback tools, and use data scraping tools to capture customer evaluation information in real time;

[0073] Real-time data update and synchronization: The data collection module sets the collection frequency according to the characteristics of different data sources. For systems with high real-time requirements (such as external market analysis platforms, social media monitoring, etc.), real-time data stream processing technology is used for data collection. For periodically updated data (such as financial data, employee assessment data, etc.), the module sets scheduled tasks, obtains data regularly through API interfaces or data transmission protocols, and ensures the timeliness and completeness of data collection.

[0074] The data preprocessing module includes:

[0075] Data cleaning: Clean the collected multi-dimensional data to remove missing values, including:

[0076] Numerical data filling: For missing values ​​in numerical data, K nearest neighbor interpolation method is used to fill in the missing values. The steps are as follows:

[0077] Suppose a data point X in the data set i In the feature vector X = {X 1 , X 2 , ..., X n} a feature x in j Missing, the missing values ​​need to be filled by the similarity of other data points;

[0078] Calculate the Euclidean distance d as a similarity measure:

[0079]

[0080] Among them, x ij and x kj is the value of the i-th and k-th data points on the j-th feature, and m is the number of feature dimensions. Select the k nearest data points and fill the missing values ​​according to their values. The filling value of the missing value is:

[0081]

[0082] in, is the missing value after filling, k is the number of neighboring points selected;

[0083] Categorical data filling: For categorical data, the mode filling method is adopted. That is, for the missing items of a certain categorical data, fill in the category value m1 that appears most frequently in this feature column. The formula is:

[0084]

[0085] where, is the missing value after filling, and m 1 is the mode of the data in this column;

[0086] Data standardization: The Z-Score standardization method is used to standardize the collected multi-dimensional data to unify the data to the same dimension;

[0087] Outlier detection: Outlier detection is performed on the collected multi-dimensional data to identify the outlier points in the data and process them. The IQR (Interquartile Range) method is used for outlier detection, which is expressed as:

[0088] IQR = Q3 - Q1;

[0089] where, Q1 is the 25th percentile of the data set, Q3 is the 75th percentile, IQR is the interquartile range, and the definition of outliers is:

[0090] x b < Q1 - 1.5×IQR or x b > Q3 + 1.5×IQR;

[0091] where, x b is a certain value in the data. If it meets the above conditions, it is regarded as an outlier, and you can choose to delete it or fill it with the mean / median.

[0092] The feature extraction module includes;

[0093] Feature extraction: The feature extraction module extracts features from the preprocessed multi-dimensional data. The extracted features include employee individual performance features, department performance features, financial data features, market and competitiveness features, and customer satisfaction features. Among them;

[0094] Employee individual performance features include the task completion rate, assessment score, work quality, etc. of employees, which measure the work efficiency and work quality of employees;

[0095] Department performance features include the production efficiency, project success rate, sales volume, etc. of the department, which reflect the overall work results and operation status of the department;

[0096] Financial data features include income, expenditure, profit, etc., which reflect the characteristics of the enterprise's financial health;

[0097] Market and competitiveness characteristics include market share, competitor data, industry trends, etc., reflecting the company's position and competitiveness in the market;

[0098] Customer satisfaction characteristics include customer ratings, feedback, customer retention rates, etc., which measure the customer's satisfaction with the company's products or services;

[0099] Feature construction: The feature extraction module enhances the expressiveness of the data by constructing new features, including interactive features and macro features, among which;

[0100] Interaction features: New features are constructed through the interaction between existing features. These interaction features can capture the nonlinear relationship or joint effect between features. It can be expressed as:

[0101] x int =x i ·x j ;

[0102] Among them, x i and x j are two features in the data set, x int as their interactive features, which help capture relationships such as the synergy between employee performance and team performance, and the interaction between market competitiveness and sales performance;

[0103] Macro features: Macro features are constructed by aggregating, summarizing or statistically analyzing existing features. These macro features help summarize the overall trend or pattern of the data. For example, a comprehensive performance score is constructed by taking a weighted average of multiple performance indicators of employees (such as task completion rate, assessment score, etc.), expressed as:

[0104] x macro =α 1 ·x 1 +α 2 ·x 2 +…+α n ·x n ;

[0105] Among them, x 1 , x 2 , ..., x n For multiple performance indicators of employees, α 1 , α 2 , ..., α n is the weight of each indicator, which is set according to the contribution of each indicator to the final performance evaluation;

[0106] Feature screening: The feature extraction module uses L1 regularization to screen features. L1 regularization can make the weights of some features approach zero through penalty terms, thereby automatically selecting the features that contribute most to performance evaluation. The specific steps are as follows:

[0107] Application of L1 regularization: By constructing a loss function containing the L1 regularization term, a regression model is trained to evaluate the importance of features. The objective function of Lasso regression is:

[0108]

[0109] in, is the predicted value, y i is the actual value, θ j is the coefficient (model parameter) of the jth feature, λ is the regularization parameter, which controls the penalty intensity of the regularization term, n1 is the number of features, and m is the number of samples;

[0110] Feature Importance Score: L1 regularization is performed by subtracting the coefficient θ of unimportant features from i Compress to zero to achieve feature selection. For features with a coefficient of zero in Lasso regression, it is considered that they do not contribute much to the model and are removed. After training, the model returns the coefficient value corresponding to each feature. The larger the absolute value of the coefficient, the more important the contribution of the feature to performance evaluation.

[0111] Feature selection criteria: Based on the model coefficient θ after L1 regularization i The absolute value of the feature is selected to have a greater impact on the model. If the coefficient of a feature |θ j | is larger, then the feature is considered to contribute more to the prediction task and is retained. Otherwise, the coefficient |θ j | Smaller features are removed.

[0112] The evaluation model building module specifically includes:

[0113] Model construction: The enterprise performance evaluation model is constructed based on the gradient boosting decision tree algorithm. The gradient boosting decision tree is an integrated learning algorithm that gradually optimizes the prediction performance of the model by constructing multiple weak learners (decision trees). In the enterprise performance evaluation scenario, the gradient boosting decision tree can handle complex nonlinear relationships and feature interaction effects, and adapt to multi-dimensional and multi-type data characteristics. The specific steps are as follows:

[0114] Select loss function: Select appropriate loss function for different prediction tasks (regression or classification). For example, for continuous performance evaluation indicators (such as production efficiency and sales), use mean square error as the loss function; for classification tasks (such as employee performance level classification), use cross entropy as the loss function;

[0115] Determine the base learner: Use the decision tree as the base learner, and set the maximum depth of the decision tree, the minimum number of sample splits and other parameters to control the model complexity;

[0116] The expression of the enterprise performance evaluation model is expressed as follows: Suppose there are N samples, the output f(x) of the model is composed of multiple base learners (i.e., decision trees), and for each sample x, the model output is:

[0117]

[0118] Where f(x) is the model’s prediction of the input sample x, M is the number of iterations (i.e., the number of decision trees in the model), η is the learning rate, which is used to control the weight of each base learner, and h m (x) is the predicted value of the mth decision tree for sample x (i.e., the output of the tree);

[0119] Model training: Train the constructed enterprise performance evaluation model. The specific steps include:

[0120] Training data input: The extracted feature data is input into the enterprise performance evaluation model for training;

[0121] Hyperparameter tuning: Optimize the hyperparameters of the enterprise performance evaluation model through grid search or random search. The key hyperparameters include:

[0122] Learning rate: controls the step size of the model update at each iteration. A smaller learning rate can improve the generalization ability of the model, but requires more trees (iterations);

[0123] Number of trees: refers to the number of decision trees used in the model;

[0124] Maximum depth: controls the depth of a single decision tree to avoid overfitting;

[0125] Subsampling ratio: Controls the random sampling ratio of sample data at each iteration, which helps prevent overfitting;

[0126] Model training: By gradually fitting the residuals, the decision tree is gradually trained to optimize the prediction error of each round;

[0127] Model evaluation: After training is completed, the performance of the enterprise performance evaluation model is evaluated. The steps include:

[0128] Cross-validation: Use cross-validation (such as K-fold cross-validation) to evaluate the model to avoid performance deviations caused by uncertainty in data partitioning. Evaluate the stability of the model by training and testing multiple times with different data partitions.

[0129] Evaluation Metrics:

[0130] Regression tasks: use mean square error, root mean square error, R 2 And other indicators to evaluate the performance of the model in the regression task;

[0131] Classification task: Use indicators such as precision, recall, F1 score, AUC-ROC, etc. to evaluate the classification effect of the model;

[0132] Model tuning: Based on the evaluation results, further optimize the model performance by adjusting hyperparameters and feature inputs.

[0133] Model application: The enterprise performance evaluation model after training and evaluation is deployed in the performance quantification management platform for real-time prediction of enterprise performance.

[0134] The performance evaluation modules include:

[0135] Model prediction: The performance evaluation module inputs real-time multi-dimensional data into the enterprise performance evaluation model, and the model outputs the predicted employee or department performance score. The enterprise performance evaluation model is based on the gradient boosting decision tree algorithm, which generates performance scores for employees or departments by gradually optimizing the prediction results. The specific steps are as follows:

[0136] Input features: The features extracted from real-time data are input into the performance evaluation model.

[0137] Prediction calculation: The model calculates the prediction result f(x) based on the input features;

[0138] Performance evaluation: Based on the prediction results output by the model, the performance evaluation module compares the predicted values ​​with the company's performance standards to evaluate the performance of employees or departments. The evaluation steps include:

[0139] Score generation: Generate employee or department performance scores based on the output of model prediction. Performance scores may be continuous values ​​(such as performance scores) or discrete values ​​(such as performance grades).

[0140] Performance rating: Performance results are divided into different levels according to the preset performance rating range. For example, performance ratings can be divided into "excellent", "good", "average", "needs improvement" and other levels.

[0141] The decision support modules include:

[0142] Obtaining performance forecast and evaluation results: The decision support module obtains the output of the performance evaluation module in real time, that is, the performance forecast results of employees or departments. The performance forecast results include:

[0143] Individual employee performance ratings (e.g., task completion rate, assessment ratings, etc.);

[0144] Evaluation results of department performance (such as production efficiency, project success rate, sales, etc.);

[0145] Integrate enterprise strategic goals and resource allocation needs: The decision support module combines the enterprise's strategic goals and resource allocation needs to ensure that decisions are consistent with the enterprise's development goals. This step includes:

[0146] Strategic goal analysis: extract short-term and long-term goals from the company's strategic plan, including financial goals (such as revenue growth, profit maximization), market goals (such as market share expansion), operational goals (such as production efficiency improvement), etc.;

[0147] Resource allocation demand analysis: Analyze the resource needs of different departments or employees based on the company's existing resources (such as manpower, capital, technology, etc.) to optimize resource allocation;

[0148] Decision reasoning based on rule engine: The decision support module uses the rule engine to infer management decisions based on performance evaluation results and corporate strategic goals, such as:

[0149] If a department’s performance score is lower than expected and the department is a key department of the enterprise, then a rule to reallocate resources or adjust strategies is triggered;

[0150] If an employee's performance is consistently excellent and in line with the company's long-term strategic goals, it may trigger promotion or reward rules;

[0151] Decision result output: The decision support module gives specific management decision suggestions based on the current performance evaluation results, strategic goals and resource requirements. The management decision suggestions include:

[0152] Plans for reallocation of resources;

[0153] Intervene, train or adjust strategies for poorly performing employees or departments;

[0154] To motivate or promote employees with outstanding performance;

[0155] Ultimately, the decision support module will feed back these decision recommendations to the company's management so that they can make corresponding strategic adjustments or implementation decisions based on the recommendations.

[0156] The visualization module includes:

[0157] Data display interface design: The visualization module provides a multi-dimensional data display interface, which allows users to clearly and intuitively view multi-dimensional data related to corporate performance. The interface includes:

[0158] Data overview: Displays an overview of the performance data of the enterprise as a whole or each department, such as a time series graph or bar chart of sales, production efficiency, employee performance, etc.;

[0159] Multi-dimensional data summary: The core data of the enterprise is displayed through the dashboard, and users can choose to view performance data of different dimensions (such as employees, departments, and projects);

[0160] Dynamic update: The interface design ensures that data is refreshed in real time, so that users can see real-time data changes and ensure timely decision-making;

[0161] Visualization of performance evaluation results: The evaluation results provided by the performance evaluation module are displayed through a visual interface, which helps managers quickly obtain useful information, including:

[0162] Heat map: Displays the performance scores of each department or employee, and displays the performance level through color depth or grading, helping managers quickly identify problem areas;

[0163] Radar chart: visualizes the multi-dimensional performance indicators of employees or departments (such as sales, project completion, customer satisfaction, etc.) to highlight their strengths and weaknesses;

[0164] Management decision visualization: The visualization module constructs decision support charts based on the management decisions provided by the decision support module. The specific steps include:

[0165] Resource allocation chart: Displays the resource allocation plan given by the decision support module based on the performance evaluation results, such as human resources and budget allocation, and uses pie charts, bar charts, etc. to display the resource proportion of each department or project;

[0166] Prioritization: Display the priorities of various tasks or projects based on strategic goals and performance evaluation, using bar charts or Gantt charts;

[0167] Scenario simulation: Through interactive charts and models, it shows the performance changes under different decision-making scenarios, such as the impact of resource adjustments and target changes on performance;

[0168] Interactive interface: The visualization module provides users with interactive functions for data, allowing managers to view data and analyze results in depth as needed. The interactive functions include:

[0169] Screening and filtering functions: Allow users to filter data by department, time, employee or other indicators to display more specific content;

[0170] Customized view: Users can customize the data display method, chart type, display range, etc. according to their needs;

[0171] Dynamic response: When users operate (such as selecting different time periods or data dimensions), the interface can be updated in real time to reflect the corresponding changes.

[0172] The visualization module also provides the function of generating reports, which supports users to export performance evaluation results and management decisions into reports in multiple formats (including PDF and Excel).

[0173] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0174] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. The enterprise digital performance quantitative management platform based on machine learning is characterized by: It includes data acquisition module, data preprocessing module, feature extraction module, evaluation model building module, performance evaluation module, decision support module and visualization display module, among which; The data collection module is used to collect multi-dimensional data related to enterprise performance from various business systems and external data sources of the enterprise in real time, and the multi-dimensional data includes individual employee performance data, department performance data, financial data, market data and customer satisfaction data; The data preprocessing module is used to preprocess the collected multi-dimensional data, preprocess data cleaning, data standardization and outlier detection; The feature extraction module is used to extract features that have an impact on enterprise performance evaluation from the pre-processed multi-dimensional data; The evaluation model building module builds an enterprise performance evaluation model through a machine learning algorithm based on the extracted features; The performance evaluation module predicts and evaluates the performance of employees or departments in real time through the enterprise performance evaluation model; The decision support module provides management decision support based on the output of the performance forecasting and evaluation module and in combination with the enterprise's strategic goals and resource allocation requirements; The visualization display module displays multi-dimensional data, performance evaluation results and management decisions through a visualization interface.

2. The enterprise digital performance quantitative management platform based on machine learning according to claim 1 is characterized in that: The data acquisition module comprises: Data source interface design: The data collection module is connected with various business systems within the enterprise and external data sources through a unified interface; Multi-dimensional data collection: The data collection module collects multi-dimensional data related to enterprise performance through the data interface; Real-time data update and synchronization: The data collection module sets the collection frequency according to the characteristics of different data sources.

3. The enterprise digital performance quantitative management platform based on machine learning according to claim 2 is characterized in that: The data preprocessing module comprises: Data cleaning: Clean the collected multi-dimensional data to remove missing values ​​in the data; Data standardization: The Z-Score standardization method is used to standardize the collected multi-dimensional data; Outlier detection: Perform outlier detection on the collected multi-dimensional data, identify abnormal points in the data, and process them.

4. The enterprise digital performance quantitative management platform based on machine learning according to claim 3 is characterized in that: The feature extraction module comprises: Feature extraction: The feature extraction module extracts features from the preprocessed multi-dimensional data. The extracted features include individual employee performance features, department performance features, financial data features, market and competitiveness features, and customer satisfaction features. Feature construction: The feature extraction module enhances the expressiveness of the data by constructing new features, including interactive features and macro features; Feature screening: The feature extraction module uses L1 regularization to screen features and select the features that contribute most to performance evaluation.

5. The enterprise digital performance quantitative management platform based on machine learning according to claim 4 is characterized in that: The evaluation model building module specifically includes: Model construction: Construct an enterprise performance evaluation model based on the gradient boosting decision tree algorithm; Model training: training the constructed enterprise performance evaluation model; Model evaluation: After training is completed, the performance of the enterprise performance evaluation model is evaluated; Model application: The enterprise performance evaluation model after training and evaluation is deployed in the performance quantification management platform for real-time prediction of enterprise performance.

6. The enterprise digital performance quantitative management platform based on machine learning according to claim 5 is characterized in that: The performance evaluation module includes: Model prediction: The performance evaluation module inputs real-time multi-dimensional data into the enterprise performance evaluation model, and the model outputs the predicted employee or department performance score; Performance evaluation: Based on the prediction results output by the model, the performance evaluation module compares the predicted values ​​with the company's performance standards to evaluate the performance of employees or departments.

7. The enterprise digital performance quantitative management platform based on machine learning according to claim 6 is characterized in that: The decision support module includes: Obtaining performance forecast results: The decision support module obtains the forecast results of the performance evaluation module in real time; Integrate enterprise strategic goals and resource allocation needs: The decision support module combines the enterprise's strategic goals and resource allocation needs to ensure that decisions are consistent with the enterprise's development goals; Decision reasoning based on rule engine: The decision support module uses the rule engine to infer management decisions based on performance evaluation results and corporate strategic goals; Decision result output: The decision support module gives management decision recommendations based on current performance evaluation results, strategic goals and resource requirements.

8. The enterprise digital performance quantitative management platform based on machine learning according to claim 7 is characterized in that: The visual display module includes: Data display interface design: The visualization module provides a multi-dimensional data display interface, which makes it easy for users to view multi-dimensional data related to corporate performance; Visualization of performance evaluation results: The evaluation results provided by the performance evaluation module are displayed through a visual interface; Management decision visualization: The visualization display module constructs decision support charts based on the management decisions provided by the decision support module; Interactive interface: The visualization module provides users with interactive functions between data.

9. The enterprise digital performance quantitative management platform based on machine learning according to claim 8 is characterized in that: The visualization module also provides a report generation function, which supports users to export performance evaluation results and management decisions into reports in multiple formats.

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