Evaluation service system and method for enterprise management

Abstract

The invention discloses an evaluation service system and method for enterprise management, and belongs to the technical field of enterprise management, and the system comprises a user information management module which is responsible for collecting, storing and managing the basic information of an enterprise and employees thereof, and is used for the storage and calling of subsequent evaluation data; and the data acquisition module is used for collecting individual information and enterprise management data of the user in the forms of questionnaires and examination questions. According to the personalized management optimization suggestion, an evaluation report generation module can generate a detailed report based on an evaluation result, the detailed report comprises a scoring chart, comparative analysis and management optimization suggestion, the system combines methods of deviation analysis, data mining, a clustering algorithm and the like, identifies weak links in management, provides personalized improvement suggestions, and improves the management efficiency. And after the management styles are classified through K-means clustering analysis, the system can automatically match the optimal practice suggestions related to the styles.

Description

Technical Field

[0001] The present invention belongs to the technical field of enterprise management, and in particular, relates to an evaluation service system and method for enterprise management. Background Art

[0002] In current enterprise management, the evaluation system usually lacks systematicity and scientificity, and it is difficult to provide accurate management improvement suggestions. Traditional enterprise management evaluation methods mainly rely on manual judgment and basic statistical data. Traditional enterprise management evaluation methods mainly focus on the evaluation of enterprise performance, employee satisfaction, leadership and organizational culture, and usually obtain data through questionnaires, interviews and assessments. Although these methods help enterprises understand the current management status to a certain extent, they also have some limitations, especially in terms of data comprehensiveness, accuracy, scientificity and personalized suggestions.

[0003] The following deficiencies exist: Single means of data collection: Existing technologies mostly rely on questionnaires, resulting in one-sided information sources, which cannot fully reflect the current status of enterprise management and easily ignore individual differences among employees and subtle differences within the enterprise; Incomplete data processing and analysis: Lack of in-depth processing and analysis of data results in the failure to effectively identify and clean up noise data or abnormal data, which directly affects the accuracy of the evaluation results; Insufficient personalized suggestions: Existing management evaluation systems usually only provide scoring or basic analysis and lack personalized management suggestions. These systems find it difficult to deeply analyze the root causes of management problems and provide effective improvement measures based on data; Weak data storage and security: Enterprise evaluation data are usually scattered in different systems, lacking unified management and secure storage, and there is a risk of data loss or leakage.

[0004] Based on this, the present invention designs an evaluation service system and method for enterprise management to solve the above problems. Summary of the invention

[0005] The purpose of the present invention is to provide an evaluation service system and method for enterprise management in order to solve the problems in the above-mentioned background technology.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] An evaluation service system for enterprise management, comprising:

[0008] The user information management module is responsible for collecting, storing and managing the basic information of the enterprise and its employees, which is used for the storage and retrieval of subsequent evaluation data;

[0009] The data collection module collects users’ individual information and enterprise management data through questionnaires and assessment questions;

[0010] The data processing and analysis module pre-processes the collected data to prepare for subsequent algorithm analysis;

[0011] Evaluation and processing module, used for quantitative analysis and qualitative evaluation of enterprise management;

[0012] Evaluation report generation module, used to generate detailed reports based on the scoring results;

[0013] The data storage and management module provides data storage and backup functions to ensure the security of evaluation data and supports rapid retrieval and call of data.

[0014] As a further description of the above technical solution: the data acquisition module supports online and offline data entry to ensure data diversity.

[0015] As a further description of the above technical solution: the evaluation processing module specifically includes the following steps:

[0016] Data standardization, using normalization methods to map data of different dimensions to the same range so that the algorithm can process it;

[0017] formula:

[0018] Where x is the original data, min(X) and max(X) are the minimum and maximum values ​​of the data set X respectively;

[0019] Indicator weight calculation, using the hierarchical analysis method to calculate the weight of each indicator, to ensure the rationality of each indicator in the comprehensive split, the hierarchical analysis method weight calculation, first construct a comparison matrix A, where A ij Indicates the relative importance of the i-th and j-th indicators;

[0020] Use the eigenvalue method to obtain the weight vector W, that is, AW = λ max W, where λ max is the maximum eigenvalue;

[0021] Comprehensive score calculation, based on indicator weights and standardized data, assuming that the system evaluation includes n indicators, the scoring formula is: where w i is the weight of the i-th indicator, x i ′ is the corresponding standardized data.

[0022] As a further description of the above technical solution: the data preprocessing includes missing value processing and outlier processing;

[0023] Missing value processing is performed using the mean filling formula method. The mean filling formula is:

[0024] Among them, x i is the non-missing value in the current feature, and n is the number of valid samples;

[0025] The Z-score method is used to process outliers. Where x is the data point, μ is the mean of the data set, σ is the standard deviation, and when Z is greater than a certain threshold, it is considered an outlier.

[0026] As a further description of the above technical solution: the data preprocessing also includes feature selection and feature engineering;

[0027] Feature selection is used to screen out the indicators that have the greatest impact on the evaluation results and improve the efficiency and accuracy of the model. The correlation coefficient method is used to calculate the correlation between the features and the target variable:

[0028] Where x and y are sample values ​​of two variables, and are their means respectively;

[0029] Feature engineering is to create new features based on existing features to improve the performance of the model; polynomial features are to increase the dimension of features and generate features in polynomial form. The quadratic polynomial formula (assuming the feature is x) is x. poiy =x+x 2 .

[0030] As a further description of the above technical solution: the generated detailed report includes scoring charts, comparative analysis, and suggestions, and the system provides personalized management optimization suggestions for enterprises.

[0031] As a further description of the above technical solution: the scoring icons include histograms, scatter plots, and box plots, which display data distribution, outliers, and correlations through visualization tools to help users intuitively understand the data.

[0032] As a further description of the above technical solution: the provision of personalized management optimization suggestions specifically includes the following steps:

[0033] 1. Analyze the evaluation results and determine the key performance indicators:

[0034] Identify strengths and weaknesses in enterprise management based on the comprehensive score calculated by the assessment processing module;

[0035] For each evaluation indicator, such as work efficiency, teamwork, innovation, management transparency, etc., analyze its specific score and industry benchmark to determine the gap with the current industry average level;

[0036] The specific deviation analysis formula used is used to calculate the degree of deviation of each indicator. Where Δi represents the deviation of the i-th indicator, x i is the score of the enterprise on this indicator. is the industry average. When Δi is negative, it means that the item needs to be optimized;

[0037] 2. Apply data mining and pattern recognition algorithms to generate suggestions:

[0038] Conduct data mining on the evaluation data and identify potential factors affecting the indicators through cluster analysis;

[0039] Cluster analysis uses the K-means algorithm to divide the enterprise evaluation data into different management characteristic groups to locate the specific management style of the enterprise;

[0040] where x i is the data point, μ i is the cluster center, d represents the distance from the data point to the cluster center, and the corresponding management optimization suggestions are matched according to the clustering;

[0041] 3. Reference industry best practices and benchmark comparison:

[0042] Retrieve industry best practice data from the database, compare the performance of various indicators of the current enterprise, and propose specific improvement measures based on the gaps;

[0043] 4. Provide personalized optimization suggestions:

[0044] Based on the above analysis results, the system generates optimization suggestions for each indicator. If the work efficiency is low, the system may suggest improving process management or introducing automation tools;

[0045] 5. Generate visual reports and prioritize recommendations:

[0046] According to the scores and optimization space of each indicator, the optimization suggestions are divided into high priority, medium priority and low priority so that the enterprise can allocate resources reasonably;

[0047] Generate visual charts, including radar charts, bar charts, and pie charts, to show the performance and room for improvement of each indicator.

[0048] An evaluation service method for enterprise management, the method comprising the following steps:

[0049] Collect, store and manage basic information of enterprises and their employees for storage and retrieval of subsequent evaluation data;

[0050] Collect users’ individual information and enterprise management data through questionnaires and assessment questions;

[0051] Preprocess the collected data to prepare for subsequent algorithm analysis;

[0052] Conduct quantitative analysis and qualitative evaluation of enterprise management;

[0053] Generate a detailed report based on the scoring results.

[0054] As a further description of the above technical solution: the data preprocessing includes missing value processing, outlier processing, feature selection and feature engineering.

[0055] Basic enterprise information covers the basic situation and operating background of the enterprise, mainly including:

[0056] Company name and industry category: used to identify the company and understand its industry environment for industry benchmark comparison;

[0057] Enterprise size: including information such as number of employees, department settings, and annual revenue, to help the system recommend appropriate management optimization measures based on enterprise size;

[0058] Organizational structure: The hierarchical structure and departmental functions of the enterprise help the system to more accurately analyze the performance of different management levels and departments;

[0059] Corporate culture and management style: By investigating the company's cultural atmosphere, leadership style (such as centralized, democratic) and other information, we can provide a basis for customized management suggestions;

[0060] Employee basic information is the most detailed part of enterprise assessment, which is used to deeply analyze individual differences and group characteristics within the enterprise, mainly including:

[0061] Employee personal information: such as age, gender, education background, time of employment, etc. This information helps to understand the basic characteristics of employees and provides a basis for subsequent performance analysis;

[0062] Position information: employee's position category, job responsibilities, years of work experience, and department, etc. Employees in different positions and departments will have significant differences in work content and performance evaluation, and the system can conduct targeted evaluations based on position information;

[0063] Work performance records: such as attendance records, work efficiency, task completion rate, etc., are used to evaluate employee performance and analyze performance influencing factors in combination with system algorithms;

[0064] Employee development plans and training experiences: including employees’ development goals, career plans, and training programs they have participated in; this information can reveal employees’ growth and development potential, and provide a reference for companies to formulate talent training strategies;

[0065] Industry benchmark information: In order to provide reasonable management comparison, the system also collects industry benchmark data, including:

[0066] Industry average performance data: such as the average work efficiency and employee turnover rate of enterprises in the industry, which are used to compare with the evaluation results to identify the advantages and room for improvement of the enterprise;

[0067] Industry best practices: Collect management methods and performance data from industry-leading companies as a reference for generating optimization suggestions to ensure the scientificity and practicality of the suggestions.

[0068] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0069] 1. In the present invention, the user information management module and the data collection module support a variety of online and offline data entry methods, and combine various forms such as questionnaires and assessment questions to ensure the diversity and integrity of the data, so that the evaluation results are more comprehensive and objective; high-precision data processing and analysis: the data processing and analysis module performs in-depth processing of the data through standardization, normalization, missing value processing, outlier processing, feature selection and other methods, effectively improving the accuracy and consistency of the evaluation results, and calculating the weight of each indicator through the hierarchical analysis method to make the comprehensive score more reasonable and ensure the appropriate weight of each indicator in the evaluation; personalized management optimization suggestions: the evaluation report generation module can generate a detailed report based on the evaluation results, including scoring charts, comparative analysis and management optimization suggestions. The system combines deviation analysis, data mining, clustering algorithms and other methods to identify weak links in management and provide personalized improvement suggestions to ensure the pertinence and effectiveness of the suggestions. After classifying the management style through K-means clustering analysis, the system will automatically match the best practice suggestions related to the style.

[0070] 2. In the present invention, the generated visualization charts are convenient for managers to quickly understand data distribution, outliers and indicator correlations, which helps enterprises make accurate decisions at different management levels; the present invention realizes a comprehensive evaluation of enterprise management through the organic combination of multi-dimensional data collection, in-depth data processing, scientific evaluation and analysis, and personalized report generation, which can effectively improve the management level and decision-making quality of enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 A schematic diagram of the system composition of an evaluation service system and method for enterprise management proposed by the present invention;

[0072] Figure 2 A schematic diagram of a method flow of an evaluation service system and method for enterprise management proposed by the present invention;

[0073] Figure 3A schematic diagram of data preprocessing of an evaluation service system and method for enterprise management proposed by the present invention. DETAILED DESCRIPTION

[0074] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0075] Please refer to the attached Figure 1 -Attached Figure 3 , the present invention provides a technical solution:

[0076] An evaluation service system for enterprise management, comprising:

[0077] The user information management module is responsible for collecting, storing and managing the basic information of the enterprise and its employees, which is used for the storage and retrieval of subsequent evaluation data;

[0078] The data collection module collects users’ individual information and enterprise management data through questionnaires and assessment questions;

[0079] The data processing and analysis module pre-processes the collected data to prepare for subsequent algorithm analysis;

[0080] Evaluation and processing module, used for quantitative analysis and qualitative evaluation of enterprise management;

[0081] Evaluation report generation module, used to generate detailed reports based on the scoring results;

[0082] The data storage and management module provides data storage and backup functions to ensure the security of evaluation data and supports rapid retrieval and call of data.

[0083] The data collection module supports online and offline data entry to ensure data diversity.

[0084] The evaluation processing module specifically includes the following steps:

[0085] Data standardization, using normalization methods to map data of different dimensions to the same range so that the algorithm can process it;

[0086] formula:

[0087] Where x is the original data, min(X) and max(X) are the minimum and maximum values ​​of the data set X respectively;

[0088] Indicator weight calculation, using the hierarchical analysis method to calculate the weight of each indicator, to ensure the rationality of each indicator in the comprehensive split, the hierarchical analysis method weight calculation, first construct a comparison matrix A, where A ij Indicates the relative importance of the i-th and j-th indicators;

[0089] Use the eigenvalue method to obtain the weight vector W, that is, AW = λ max W, where λ max is the maximum eigenvalue;

[0090] Comprehensive score calculation, based on indicator weights and standardized data, assuming that the system evaluation includes n indicators, the scoring formula is: where w i is the weight of the i-th indicator, x i ′ is the corresponding standardized data.

[0091] Data preprocessing includes missing value processing and outlier processing;

[0092] Missing value processing is performed using the mean filling formula method. The mean filling formula is:

[0093] Among them, x i is the non-missing value in the current feature, and n is the number of valid samples;

[0094] The Z-score method is used to process outliers. Where x is the data point, μ is the mean of the data set, σ is the standard deviation, and when Z is greater than a certain threshold, it is considered an outlier.

[0095] Data preprocessing also includes feature selection and feature engineering;

[0096] Feature selection is used to screen out the indicators that have the greatest impact on the evaluation results and improve the efficiency and accuracy of the model. The correlation coefficient method is used to calculate the correlation between the features and the target variable:

[0097] Where x and y are sample values ​​of two variables, and are their means respectively;

[0098] Feature engineering is to create new features based on existing features to improve the performance of the model; polynomial features are to increase the dimension of features and generate features in polynomial form. The quadratic polynomial formula (assuming the feature is x) is x. poiy =x+x 2 .

[0099] The generated detailed report includes scoring charts, comparative analysis, and recommendations, and the system provides enterprises with personalized management optimization suggestions.

[0100] Scoring icons include histograms, scatter plots, and box plots, which use visualization tools to display data distribution, outliers, and correlations to help users understand the data intuitively.

[0101] Providing personalized management optimization suggestions specifically includes the following steps:

[0102] 1. Analyze the evaluation results and determine the key performance indicators:

[0103] Identify strengths and weaknesses in enterprise management based on the comprehensive score calculated by the assessment processing module;

[0104] For each evaluation indicator, such as work efficiency, teamwork, innovation, management transparency, etc., analyze its specific score and industry benchmark to determine the gap with the current industry average level;

[0105] The specific deviation analysis formula used is used to calculate the degree of deviation of each indicator. Where Δi represents the deviation of the i-th indicator, x i is the score of the enterprise on this indicator. is the industry average. When Δi is negative, it means that the item needs to be optimized;

[0106] 2. Apply data mining and pattern recognition algorithms to generate suggestions:

[0107] Conduct data mining on the evaluation data and identify potential factors affecting the indicators through cluster analysis;

[0108] Cluster analysis uses the K-means algorithm to divide the enterprise evaluation data into different management characteristic groups to locate the specific management style of the enterprise;

[0109] where x i is the data point, μ i is the cluster center, d represents the distance from the data point to the cluster center, and the corresponding management optimization suggestions are matched according to the clustering;

[0110] 3. Reference industry best practices and benchmark comparison:

[0111] Retrieve industry best practice data from the database, compare the performance of various indicators of the current enterprise, and propose specific improvement measures based on the gaps;

[0112] 4. Provide personalized optimization suggestions:

[0113] Based on the above analysis results, the system generates optimization suggestions for each indicator. If the work efficiency is low, the system may suggest improving process management or introducing automation tools;

[0114] 5. Generate visual reports and prioritize recommendations:

[0115] According to the scores and optimization space of each indicator, the optimization suggestions are divided into high priority, medium priority and low priority so that the enterprise can allocate resources reasonably;

[0116] Generate visual charts, including radar charts, bar charts, and pie charts, to show the performance and room for improvement of each indicator.

[0117] An evaluation service method for enterprise management, the method comprising the following steps:

[0118] Collect, store and manage basic information of enterprises and their employees for storage and retrieval of subsequent evaluation data;

[0119] Collect users’ individual information and enterprise management data through questionnaires and assessment questions;

[0120] Preprocess the collected data to prepare for subsequent algorithm analysis;

[0121] Conduct quantitative analysis and qualitative evaluation of enterprise management;

[0122] Generate a detailed report based on the scoring results.

[0123] The data preprocessing includes missing value processing, outlier processing, feature selection and feature engineering.

[0124] 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. An evaluation service system for enterprise management, characterized in that: include: The user information management module is responsible for collecting, storing and managing the basic information of the enterprise and its employees, which is used for the storage and retrieval of subsequent evaluation data; The data collection module collects users’ individual information and enterprise management data through questionnaires and assessment questions; The data processing and analysis module pre-processes the collected data to prepare for subsequent algorithm analysis; Evaluation and processing module, used for quantitative analysis and qualitative evaluation of enterprise management; Evaluation report generation module, used to generate detailed reports based on the scoring results; The data storage and management module provides data storage and backup functions, ensures the security of evaluation data, and supports rapid retrieval and call of data.

2. The evaluation service system for enterprise management according to claim 1, characterized in that: The data collection module supports online and offline data entry to ensure data diversity.

3. The evaluation service system for enterprise management according to claim 1, characterized in that: The evaluation processing module specifically includes the following steps: Data standardization, using normalization methods to map data of different dimensions to the same range so that the algorithm can process it; official: Where x is the original data, min(X) and max(X) are the minimum and maximum values ​​of the data set X respectively; Indicator weight calculation, use the hierarchical analysis method to calculate the weight of each indicator to ensure the rationality of each indicator in the comprehensive division. The hierarchical analysis method weight calculation first constructs a comparison matrix A, where A ij Indicates the relative importance of the i-th and j-th indicators; Use the eigenvalue method to obtain the weight vector W, that is, AW = λ max W, where λ max is the maximum eigenvalue; Comprehensive score calculation, based on indicator weights and standardized data, assuming that the system evaluation includes n indicators, the scoring formula is: where w i is the weight of the i-th indicator, x i ′ is the corresponding standardized data.

4. The evaluation service system for enterprise management according to claim 1, characterized in that: The data preprocessing includes missing value processing and outlier processing; Missing value processing is performed using the mean filling formula method. The mean filling formula is: Among them, x i is the non-missing value in the current feature, and n is the number of valid samples; The outlier processing adopts the Z score method formula. Where x is a data point, μ is the mean of the data set, σ is the standard deviation, and when |Z| is greater than a certain threshold, it is considered an outlier.

5. The evaluation service system for enterprise management according to claim 1, characterized in that: The data preprocessing also includes feature selection and feature engineering; Feature selection is used to screen out the indicators that have the greatest impact on the evaluation results and improve the efficiency and accuracy of the model. The correlation coefficient method is used to calculate the correlation between the features and the target variable: Where x and y are sample values ​​of two variables, and are their means respectively; feature Engineering is the process of creating new features based on existing features to improve the performance of the model; Polynomial features, increase the dimension of features, generate features in polynomial form, quadratic polynomial formula (assuming the feature is x), x poiy =x+x 2 .

6. The evaluation service system for enterprise management according to claim 1, characterized in that: The generated detailed report includes scoring charts, comparative analysis, and suggestions, and the system provides enterprises with personalized management optimization suggestions.

7. The evaluation service system for enterprise management according to claim 6, characterized in that: The scoring icons include histograms, scatter plots, and box plots, which use visualization tools to display data distribution, outliers, and correlations to help users understand the data intuitively.

8. The evaluation service system for enterprise management according to claim 7, characterized in that: The providing of personalized management optimization suggestions specifically includes the following steps:

1. Analyze the evaluation results and determine the key performance indicators: Identify strengths and weaknesses in enterprise management based on the comprehensive score calculated by the assessment processing module; For each evaluation indicator, such as work efficiency, teamwork, innovation, management transparency, etc., analyze its specific score and industry benchmark to determine the gap with the current industry average level; The specific deviation analysis formula used is used to calculate the degree of deviation of each indicator. Where Δi represents the deviation of the i-th indicator, x i is the score of the enterprise on this indicator. is the industry average. When Δi is negative, it means that the item needs to be optimized; 2. Apply data mining and pattern recognition algorithms to generate suggestions: Conduct data mining on the evaluation data and identify potential factors affecting the indicators through cluster analysis; Cluster analysis uses the K-means algorithm to divide the enterprise evaluation data into different management characteristic groups to locate the specific management style of the enterprise; where x i is the data point, μ i is the cluster center, d represents the distance from the data point to the cluster center, and the corresponding management optimization suggestions are matched according to the clustering; 3. Reference industry best practices and benchmark comparison: Retrieve industry best practice data from the database, compare the performance of various indicators of the current enterprise, and propose specific improvement measures based on the gaps; 4. Provide personalized optimization suggestions: Based on the above analysis results, the system generates optimization suggestions for each indicator. If the work efficiency is low, the system may suggest improving process management or introducing automation tools; 5. Generate visual reports and prioritize recommendations: According to the scores and optimization space of each indicator, the optimization suggestions are divided into high priority, medium priority and low priority so that the enterprise can allocate resources reasonably; Generate visual charts, including radar charts, bar charts, and pie charts, to show the performance and room for improvement of each indicator.

9. An evaluation service method for enterprise management, according to an evaluation service system for enterprise management according to any one of claims 1 to 8, characterized in that: The method comprises the following steps: Collect, store and manage basic information of enterprises and their employees for storage and retrieval of subsequent evaluation data; Collect users’ individual information and enterprise management data through questionnaires and assessment questions; Preprocess the collected data to prepare for subsequent algorithm analysis; Conduct quantitative analysis and qualitative evaluation of enterprise management; Generate a detailed report based on the scoring results.

10. The evaluation service method for enterprise management according to claim 9, characterized in that: The data preprocessing includes missing value processing, outlier processing, feature selection and feature engineering.

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