Public institution evaluation method, system and device and storage medium
Through big data and AI technology, combined with internal business data and external public opinion, public institutions evaluation methods are established, which solves the problem of existing evaluation methods neglecting public opinion feedback and high labor costs, and achieves efficient and accurate comprehensive evaluation.
Patent Information
- Application Number
- CN202410333695.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-03-22
- Publication Date
- 2024-12-27
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
The existing public institutions' evaluation methods rely on internal financial reports and annual operating data, ignore external public feedback, resulting in a lack of scientific and reasonable comprehensive evaluation methods, and it consumes a lot of labor costs when analyzing policy documents.
Using big data and AI technology, we collect business data, establish standardized data models, perform preprocessing and model training, and evaluate it in combination with network public opinion data to output the comprehensive evaluation results of public institutions.
It has achieved a comprehensive evaluation of public institutions, improved evaluation efficiency and accuracy, and can objectively analyze departmental annual reports and online public opinion, assisted in annual reports and digital management.
Smart Images
Figure CN119204769A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of computer learning technology, and in particular to a public institution evaluation method, system, device and storage medium. Background Art
[0002] The evaluation of public institutions mostly relies on internal financial reports and annual operating data, while ignoring external public opinion feedback. With the development of the Internet and social media, the public's views and evaluations of public institutions can spread rapidly on the Internet, which has a significant impact on the social image and reputation of public institutions. Therefore, it is necessary to develop an evaluation method that can integrate internal data and external public opinion to more comprehensively evaluate the performance of public institutions.
[0003] In addition, when analyzing and sorting out policy documents, it takes a lot of time and manpower to fully understand the huge number of documents. Therefore, there is currently a lack of a scientific and reasonable public institution evaluation method. Summary of the invention
[0004] The present application provides a public institution evaluation method, system, device and storage medium to solve the above-mentioned problems.
[0005] On the one hand, the present application provides a public institution evaluation method, which includes the following steps: Step S1: Collect business data and establish a standardized data model; Step S2: Preprocess the target data in the standardized data model; Step S3: Input the preprocessed data into a pre-built evaluation model for model training and testing; Step S4: Input the relevant data of the public institution to be evaluated into the trained evaluation model for prediction, and output the evaluation results of the public institution to be evaluated.
[0006] In one implementation of the present application, the step S1 specifically includes: step S11: obtaining organizational structure data based on business data; wherein the organizational structure data includes: leadership position registration data, organizational data, establishment data, responsibility data and leadership position adjustment data; step S12: analyzing the organizational structure data based on the existing business data model and formulating a big data storage structure; step S13: generating the standardized data model based on the big data storage structure.
[0007] In one implementation of the present application, the step S2 is specifically as follows: step S21: constructing a data extraction function based on data warehouse technology; step S22: extracting, cleaning and converting the target data through the data extraction function and preset data extraction rules; step S23: verifying the consistency of the source data and the target data platform data.
[0008] In one implementation of the present application, before step S3, the method further includes: extracting organizational construction and performance data from the preprocessed data; wherein the organizational construction and performance data include: the number of organizational construction paragraphs, the number of organizational construction keywords, the number of innovation and excellence paragraphs, the number of innovation and excellence keywords, the number of performance paragraphs, and the number of performance keywords; performing one-hot encoding on the funding source field; extracting the total net assets at the end of the year, the amount of asset changes, and the balance amount from the text description information of the asset profit and loss situation.
[0009] In one implementation of the present application, the method also includes: calculating whether the relevant qualifications have expired based on the description of the situation of the relevant assets; calculating the data on changes in the establishment and number of employees; wherein the data on changes in the number of employees include: the number of employees as a percentage of the establishment number, the change in the number of establishment this year compared to last year, and the change in the number of employees this year compared to last year; processing null values and missing items in the data, and repairing missing values; wherein the methods for repairing missing values include: deleting samples, filling in average values, and filling in default values.
[0010] In one implementation of the present application, the model training process is specifically as follows: using the target data as the initial features, analyzing the initial features to obtain feature correlation coefficient rankings; ranking the importance of features obtained based on the feature_importances_() method in the Gradient boosting regression algorithm; training multiple models based on the regression algorithm, and selecting the optimal model.
[0011] In one implementation of the present application, the optimal model is selected, specifically including: debugging the training machine parameters of multiple models; based on the mean square error method, selecting the model with the highest MSE score on the test set as the final model.
[0012] The present application also provides a public institution evaluation system, which includes: a data collection module for collecting business data and establishing a standardized data model; a preprocessing module for preprocessing target data in the standardized data model; a model training module for inputting the preprocessed data into a pre-built evaluation model for model training and testing; an output prediction module for inputting relevant data of the public institution to be evaluated into the trained evaluation model for prediction, and outputting the evaluation results of the public institution to be evaluated.
[0013] The present application also provides a public institution evaluation device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to: collect business data and establish a standardized data model; pre-process target data in the standardized data model; input the pre-processed data into a pre-built evaluation model for model training and testing; input relevant data of the public institution to be evaluated into the trained evaluation model for prediction, and output the evaluation results of the public institution to be evaluated.
[0014] Finally, the present application also provides a non-volatile computer storage medium for public institution evaluation, storing computer executable instructions, wherein the computer executable instructions are configured to: collect business data and establish a standardized data model; preprocess the target data in the standardized data model; input the preprocessed data into a pre-built evaluation model for model training and testing; input the relevant data of the public institution to be evaluated into the trained evaluation model for prediction, and output the evaluation results of the public institution to be evaluated.
[0015] This application provides a public institution evaluation method, system, equipment and storage medium, which are more efficient by using big data and AI technology to process and analyze data. Use multiple data model training to select the optimal model to ensure the accuracy of the evaluation. By modeling and training historical data and evaluating the current situation, the unit can be objectively evaluated in the system, and the relevant data of the department's annual report and the status of online public opinion can be analyzed and discovered from big data, which can be used to assist in the collection of annual reports and online public opinion, and realize the digital management of the government. BRIEF DESCRIPTION OF THE DRAWINGS
[0016] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:
[0017] Figure 1 A flow chart of a public institution evaluation method provided in an embodiment of the present application;
[0018] Figure 2 Provided in the embodiments of this application
[0019] Figure 3 A composition diagram of a public institution evaluation system provided in an embodiment of the present application;
[0020] Figure 4 A schematic diagram of a public institution evaluation device provided in an embodiment of the present application. DETAILED DESCRIPTION
[0021] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.
[0022] The embodiments of the present application provide a public institution evaluation method, system, device and storage medium. The technical solutions proposed in the embodiments of the present application are described in detail below with reference to the accompanying drawings.
[0023] Figure 1 A flow chart of a public institution evaluation method provided in an embodiment of the present application. Figure 1 As shown, the method mainly includes the following steps:
[0024] Step S1: Collect business data and establish a standardized data model.
[0025] Step S2: pre-processing the target data in the standardized data model.
[0026] Step S3: Input the preprocessed data into the pre-built evaluation model for model training and testing.
[0027] Step S4: inputting relevant data of the public institution to be evaluated into the trained evaluation model for prediction, and outputting the evaluation result of the public institution to be evaluated.
[0028] In an embodiment of the present application, the step S1 specifically includes: step S11: obtaining organizational structure data based on business data; wherein the organizational structure data includes: leadership position registration data, organizational data, establishment data, responsibility data and leadership position adjustment data; step S12: analyzing the organizational structure data based on the existing business data model and formulating a big data storage structure; step S13: generating the standardized data model based on the big data storage structure.
[0029] In an embodiment of the present application, the step S2 is specifically as follows: step S21: constructing a data extraction function based on data warehouse technology; step S22: extracting, cleaning and converting the target data through the data extraction function and preset data extraction rules; step S23: verifying the consistency of the source data and the target data platform data.
[0030] In an embodiment of the present application, before step S3, the method also includes: extracting organizational construction and performance data from the preprocessed data; wherein the organizational construction and performance data include: the number of organizational construction paragraphs, the number of organizational construction keywords, the number of innovation and excellence paragraphs, the number of innovation and excellence keywords, the number of performance paragraphs, and the number of performance keywords; performing one-hot encoding on the funding source field; extracting the total net assets at the end of the year, the amount of asset changes, and the balance amount from the text description information of the asset profit and loss situation.
[0031] In an embodiment of the present application, the method also includes: calculating whether the relevant qualifications have expired based on the description of the situation of the relevant assets; calculating the data on changes in the establishment and the number of employees; wherein the data on changes in the number of employees include: the number of employees as a percentage of the establishment number, the change in the number of establishment this year compared to last year, and the change in the number of employees this year compared to last year; processing null values and missing items in the data, and repairing missing values; wherein the methods for repairing missing values include: deleting samples, filling in average values, and filling in default values.
[0032] In the embodiment of the present application, the process of model training is specifically as follows: taking the target data as the initial features, analyzing the initial features to obtain the feature correlation coefficient ranking; ranking the importance of the features obtained by the feature_importances_() method in the Gradient boosting regression algorithm; training multiple models based on the regression algorithm, and selecting the optimal model.
[0033] The data disclosed in the unit's annual report and the data items extracted from the data preprocessing part are used as the initial features of the model. The Pearson correlation coefficient method can be used to calculate the correlation between features and labels, which is used to identify the correlation coefficient of features and facilitate understanding of the information gain ranking of features in the learning process. Through image libraries such as matplotlib and seaborn, the following can be obtained: Figure 2 Feature correlation coefficient ranking plot shown.
[0034] In an embodiment of the present application, selecting the optimal model specifically includes: debugging the training machine parameters of multiple models; based on the mean square error method, selecting the model with the highest MSE score on the test set as the final model.
[0035] The above is a public institution evaluation method provided by the embodiment of the present application. Based on the same inventive concept, the embodiment of the present application also provides a public institution evaluation system. Figure 3 A diagram of a public institution evaluation system provided in an embodiment of the present application, such as Figure 3As shown, the system mainly includes: a data collection module 301, which is used to collect business data and establish a standardized data model; a preprocessing module 302, which is used to preprocess the target data in the standardized data model; a model training module 303, which is used to input the preprocessed data into a pre-built evaluation model for model training and testing; an output prediction module 304, which is used to input the relevant data of the public institution to be evaluated into the trained evaluation model for prediction, and output the evaluation result of the public institution to be evaluated.
[0036] The above is a public institution evaluation system provided by the embodiment of the present application. Based on the same inventive concept, the embodiment of the present application also provides a public institution evaluation device. Figure 4 A schematic diagram of a public institution evaluation device provided in an embodiment of the present application, such as Figure 4 As shown, the device mainly includes: at least one processor 401; and a memory 402 communicatively connected to the at least one processor; wherein the memory 402 stores instructions executable by the at least one processor 401, and the instructions are executed by the at least one processor 401 so that the at least one processor 401 can complete: collecting business data and establishing a standardized data model; preprocessing the target data in the standardized data model; inputting the preprocessed data into a pre-built evaluation model for model training and testing; inputting the relevant data of the public institution to be evaluated into the trained evaluation model for prediction, and outputting the evaluation results of the public institution to be evaluated.
[0037] In addition, an embodiment of the present application also provides a non-volatile computer storage medium for public institution evaluation, storing computer executable instructions, wherein the computer executable instructions are configured to: collect business data and establish a standardized data model; preprocess the target data in the standardized data model; input the preprocessed data into a pre-built evaluation model for model training and testing; input the relevant data of the public institution to be evaluated into the trained evaluation model for prediction, and output the evaluation results of the public institution to be evaluated.
[0038] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.
[0039] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.
[0040] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.
[0041] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.
[0042] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.
[0043] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.
[0044] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims
1. A method for evaluating a public institution, characterized in that: The method comprises the following steps: Step S1: Collect business data and establish a standardized data model; Step S2: preprocessing the target data in the standardized data model; Step S3: input the preprocessed data into the pre-built evaluation model for model training and testing; Step S4: inputting the relevant data of the public institution to be evaluated into the trained evaluation model for prediction, and outputting the evaluation result of the public institution to be evaluated.
2. A method for evaluating a public institution according to claim 1, characterized in that: The step S1 specifically includes: Step S11: Based on the business data, obtain the organization establishment data; wherein the organization establishment data includes: leadership position registration data, organization data, establishment data, responsibility data and leadership position adjustment data; Step S12: Analyze the organization establishment data based on the existing business data model and formulate a big data storage structure; Step S13: Generate the standardized data model based on the big data storage structure.
3. A method for evaluating a public institution according to claim 1, characterized in that: The step S2 is specifically: Step S21: constructing a data extraction function based on data warehouse technology; Step S22: extracting, cleaning and converting the target data through the data extraction function and preset data extraction rules; Step S23: Verify the consistency of source data and target data platform data.
4. A method for evaluating a public institution according to claim 1, characterized in that: Before step S3, the method further includes: Extracting the organizational construction and job performance data from the pre-processed data; wherein the organizational construction and job performance data includes: the number of organizational construction paragraphs, the number of organizational construction keywords, the number of innovation and excellence paragraphs, the number of innovation and excellence keywords, the number of job performance paragraphs, and the number of performance keywords; One-hot encoding is performed on the funding source field; Extract the total net assets at the end of the year, the amount of asset changes, and the balance amount from the text description of the asset profit and loss situation.
5. A method for evaluating a public institution according to claim 4, characterized in that: The method further comprises: Calculate whether the relevant qualifications have expired based on the description of the relevant assets; Calculate the data on changes in the number of establishments and employees; the data on changes in the number of employees include: the number of employees as a percentage of the number of establishments, the change in the number of establishments this year compared to last year, and the change in the number of employees this year compared to last year; Process the empty values and missing items in the data and repair the missing values; the methods of repairing missing values include: deleting samples, filling the mean value, and filling the default value.
6. A method for evaluating a public institution according to claim 1, characterized in that: The model training process is as follows: Taking the target data as initial features, analyzing the initial features to obtain a feature correlation coefficient ranking; The importance ranking of features obtained by the feature_importances_() method in the Gradient boosting regression algorithm; Multiple models are trained based on regression algorithms and the optimal model is selected.
7. A method for evaluating a public institution according to claim 6, characterized in that: Select the best model, including: Debug training machine parameters for multiple models; Based on the mean square error method, the model with the highest MSE score on the test set is selected as the final model.
8. A public institution evaluation system, characterized in that: The system comprises: Data collection module, used to collect business data and establish standardized data models; A preprocessing module, used for preprocessing the target data in the standardized data model; Model training module, used to input preprocessed data into a pre-built evaluation model for model training and testing; The output prediction module is used to input the relevant data of the public institution to be evaluated into the trained evaluation model for prediction, and output the evaluation result of the public institution to be evaluated.
9. A public institution evaluation device, characterized in that The device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can: collect business data and establish a standardized data model; preprocess the target data in the standardized data model; input the preprocessed data into a pre-built evaluation model for model training and testing; input relevant data of the public institution to be evaluated into the trained evaluation model for prediction, and output the evaluation results of the public institution to be evaluated.
10. A non-volatile computer storage medium for public institution evaluation, storing computer executable instructions, characterized in that: The computer executable instructions are configured to: collect business data and establish a standardized data model; preprocess the target data in the standardized data model; input the preprocessed data into a pre-built evaluation model for model training and testing; input the relevant data of the public institution to be evaluated into the trained evaluation model for prediction, and output the evaluation results of the public institution to be evaluated.