Hospital intelligent management rating empirical material generation system and method based on RPA

Abstract

The invention relates to the technical field of medical information, in particular to a hospital intelligent management rating empirical material generation system and method based on RPA. The system comprises a user operation data acquisition module, a hospital intelligent management rating index setting module, a screenshot automatic interception module, a material collection module, a file generation module and an automatic closing toolbar and tray application module. According to the RPA-based hospital intelligent management rating empirical material generation system and method provided by the invention, through an RPA technology, automatic operation of a hospital information system can be realized, manual intervention is reduced, the speed and accuracy of data processing are improved, the error rate is reduced, and the working efficiency is improved. The method has the advantages of high efficiency, low error, resource configuration optimization, high data quality, support of continuous improvement and the like.

Description

Technical Field

[0001] The present invention relates to the field of medical information technology, and in particular to a system and method for generating empirical materials for hospital smart management rating based on RPA. Background Art

[0002] With the rapid development of information technology, hospital smart management is gradually transforming into intelligence. The concept of hospital smart management has emerged, and its core lies in improving the quality of medical services and management efficiency through information and intelligent means. As an important part of the construction of smart hospitals, hospital smart management aims to achieve the optimal allocation of hospital resources and efficient operation of management processes through refined and intelligent management tools.

[0003] During this transformation process, hospitals need to collect and organize a large amount of empirical materials to prove the implementation effect and level of their smart hospitals. These materials include data and operation records in various medical information systems such as medical care management system, human resources management system, financial asset management system, equipment and facility management system, drug and consumables management system, operation management system, operation guarantee management system, teaching and scientific research management system, office management system, basic and security system, etc. These materials usually rely on manual operation to obtain, including manual login to the system, querying data, exporting reports, screenshot operation interface, etc., which is not only inefficient, but also has a high error rate, making it difficult to meet the needs of hospital smart management rating.

[0004] To this end, a system and method for generating empirical materials for hospital smart management rating based on RPA was proposed. Summary of the invention

[0005] Based on this, it is necessary to provide an RPA-based hospital smart management rating empirical material generation system and method to address the above technical issues.

[0006] According to a first aspect of the present invention, a system for generating empirical materials for hospital smart management rating based on RPA is provided, including: a user operation data acquisition module, which is used to capture user operation step data related to hospital smart management rating in a medical information system based on RPA technology, AI technology and multimodal data fusion technology, and identify system pages contained in the user operation step data and key information contained in the system pages; a hospital smart management rating indicator setting module, which is used to obtain rating indicators related to hospital smart management rating, associate the rating indicators with user operation step data based on RPA technology, and generate a comparison table of rating indicators and system pages; a screenshot automatic capture module, which is used to identify and determine the target rating indicators that need to be screenshotted based on the preset rating indicators of hospital smart management rating, locate and match the system pages associated with the target rating indicators based on the comparison table of rating indicators and system pages, and screenshot the key information contained in the associated system pages based on intelligent screenshot optimization technology to obtain screenshot image data, and perform type annotation on the screenshot image data to obtain screenshot image annotation data including the target rating indicators; a material collection module, which is used to obtain the information related to the screenshot. The module classifies the screenshot image annotation data and the evidence material data according to the data type and the target rating index to obtain multiple classified data, extracts the classified data to obtain the extracted data, archives the extracted data according to the target rating requirements, the target rating index and the data type, and generates index information and directory information; the file generation module is used to create file templates in various formats that meet the hospital smart management rating requirements, uses the template engine technology to match the extracted data with the predefined placeholders in the file template to obtain an automatically filled text file, performs style and typesetting processing on the text file based on the formatting tool to obtain an initial evidence material text file that meets the target rating requirements, and performs quality verification on the initial evidence material text file to obtain a target evidence material text file; the module automatically closes the toolbar and tray application, which is used to identify the system page and operation step sequence information that the user is currently or about to operate based on the user operation step data, and identifies the active toolbar and / or tray application based on the system page and operation step sequence information that the user is currently or about to operate, and closes the inactive toolbar and / or tray application.

[0007] Optionally, the user operation data acquisition module includes: a system initialization and configuration submodule, which is used for users to input configuration information related to the hospital smart management rating through a graphical user interface, and the configuration information includes the login credential information of the medical information system and the operation step sequence information for monitoring the user to complete the target task; the medical information system includes a medical care management system, a human resources management system, a financial asset management system, an equipment and facility management system, a drug and consumables management system, an operation management system, an operation guarantee management system, a teaching and scientific research management system, an office management system, and a basic and security system; a user operation capture submodule is used to automatically log in to the medical information system and monitor the user's completion based on the login credential information of the medical information system based on RPA technology. The operation steps of the target task are captured based on multimodal data fusion technology to capture the user operation step data related to the hospital smart management rating and the contextual environment information of the corresponding user operation steps; the AI ​​automatic recognition and recording sub-module is used to identify the system pages and the key information contained in the user operation step data based on the trained user behavior recognition AI model and natural language processing technology, and store the system pages, the key information contained in the system pages, the timestamps of the corresponding user operation steps and the contextual environment information of the corresponding user operation steps in the database; the intelligent learning and optimization sub-module is used to build and train the user behavior recognition AI model to obtain the trained user behavior recognition AI model.

[0008] Optionally, the user operation capture submodule also includes: a context perception and multimodal data processing submodule, which is used to obtain the user's operation step sequence information in the medical information system through log analysis and behavior tracking technology, and based on image processing technology, capture the system page during the user's operation steps and the text information in the corresponding system page according to the operation step sequence information, and parse the text information through natural language processing technology to obtain the user's operation step intention information and the corresponding user operation step context environment information, analyze the system page, the user's operation intention information and the corresponding user operation step context environment information, and obtain user operation step data related to the hospital smart management rating.

[0009] Optionally, the hospital smart management rating index setting module includes: a rating index data integration submodule, which is used to obtain initial rating indicators related to the hospital smart management rating from the website of the National Health Commission; a rating index parsing and mapping submodule, which is used to form a mapping relationship between the initial rating indicators and key information based on the preset mapping rules between the indicators and key information; a user configuration and customization submodule, which is used to select preset rating indicators from the initial rating indicators according to the target rating requirements of the hospital smart management rating, and assign corresponding weight values ​​to the preset rating indicators; a rating index dynamic adjustment submodule, which is used to dynamically adjust the preset rating indicators and corresponding weight values ​​according to the feedback information of the hospital smart management rating and the latest rating indicators related to the hospital smart management rating; an intelligent association and comparison table generation submodule, which is used to associate the preset rating indicators with the user operation step data based on RPA technology, and generate a comparison table of rating indicators and system pages; a rating index review and release submodule, which is used to review the comparison table of rating indicators and system pages and the preset rating indicators, and publish the preset rating indicators after the review is passed.

[0010] Optionally, the automatic screenshot capture module includes: a rating indicator identification and locking submodule, which is used to identify and determine the target rating indicator that needs to be screenshotted according to the preset rating indicator of the hospital smart management rating; a system page comparison table matching submodule, which is used to locate and match the system page associated with the target rating indicator according to the comparison table of rating indicators and system pages, and identify and determine the system page that needs to be screenshotted; an automatic login and navigation submodule, which is used to automatically log in to the medical information system based on the login credential information of the medical information system based on RPA technology, and navigate to the corresponding system page according to the comparison table of rating indicators and system pages; an intelligent screenshot execution submodule, which is used to execute the screenshot in the guide After navigating to the corresponding system page, the attribute information of the corresponding system page is identified, and based on the attribute information of the corresponding system page, the page elements of the corresponding system page are analyzed to identify at least one area range containing key information, and based on the area range containing the key information, according to the content complexity of the corresponding system page, the resolution and timing of the screenshot are automatically adjusted, and the area range containing the key information is screenshot processed to obtain at least one screenshot image data; the screenshot data annotation and classification submodule is used to annotate the screenshot image data by type, obtain screenshot image annotation data including target rating indicators, and classify and store the screenshot image annotation data according to the target rating indicators.

[0011] Optionally, the material collection module includes: a material identification and classification submodule, which is used to obtain proof material data related to the screenshot image annotation data, wherein the proof material data is business data related to the screenshot image annotation data, and the screenshot image annotation data and the proof material data are classified and processed according to the target rating index to obtain the first classified data classified under the target rating index, and the first classified data is classified according to the data type to obtain the second classified data corresponding to each data type under each target rating index; a data extraction and sorting submodule, which is used to extract data from the second classified data to obtain extracted data, wherein the extracted data includes key information extracted from the screenshot image annotation data in the second classified data and proof information related to the key information extracted from the proof material data in the second classified data, and the extracted data is structured format data; a material archiving and indexing submodule, which is used to archive the extracted data according to the target rating requirements, target rating indicators and data types, and generate index information and directory information.

[0012] Optionally, the file generation module includes: a template preparation and customization sub-module, which is used to create file templates in various formats that meet the requirements of hospital smart management rating; a data binding and automatic filling sub-module, which is used to match the extracted data with predefined placeholders in the file template based on the template engine technology, according to the target rating requirements, target rating indicators and data types, to obtain an automatically filled text file, wherein the target rating requirements, target rating indicators and data types correspond to different types of placeholders respectively; a formatting and typesetting sub-module, which is used to perform style and typesetting processing on the text file based on the formatting tool, to obtain an initial empirical material text file that meets the target rating requirements; a data verification and quality control sub-module, which is used to obtain detailed information related to the hospital smart management rating, and to perform quality verification on the initial empirical material text file based on the detailed information to obtain the target empirical material text file; a file preservation and version management sub-module, which is used to store and version manage the target empirical material text file.

[0013] According to a second aspect of the present invention, a method for generating empirical materials for hospital smart management rating based on RPA is provided, comprising: capturing user operation step data related to hospital smart management rating in a medical information system through a user operation data acquisition module according to RPA technology, AI technology and multimodal data fusion technology, and identifying system pages contained in the user operation step data and key information contained in the system pages; acquiring rating indicators related to hospital smart management rating through a hospital smart management rating indicator setting module, associating the rating indicators with user operation step data based on RPA technology, and generating a comparison table of rating indicators and system pages; identifying and determining the target rating indicators that need to be screenshotted according to the preset rating indicators of hospital smart management rating through an automatic screenshot capture module, positioning and matching the system pages associated with the target rating indicators based on the comparison table of rating indicators and system pages, and screenshotting the key information contained in the associated system pages based on intelligent screenshot optimization technology to obtain screenshot image data, and annotating the screenshot image data by type to obtain screenshot image annotation data including the target rating indicators; acquiring evidence materials related to the screenshot image annotation data through a material collection module Data, the evidence material data is business data related to the screenshot image annotation data, according to the data type and the target rating index, the screenshot image annotation data and the evidence material data are classified and processed to obtain multiple classified data, the classified data are extracted to obtain extracted data, and the extracted data are archived according to the target rating requirements, the target rating indicators and the data type, and index information and directory information are generated; through the file generation module, multiple format file templates that meet the hospital smart management rating requirements are created, and the template engine technology is used to match the extracted data with the predefined placeholders in the file template to obtain an automatically filled text file, based on the formatting tool, the text file is styled and formatted to obtain an initial empirical material text file that meets the target rating requirements, and the initial empirical material text file is quality verified to obtain a target empirical material text file; through the automatic closing toolbar and tray application module, according to the user operation step data, the system page and operation step sequence information that the user is currently or about to operate are identified, and according to the system page and operation step sequence information that the user is currently or about to operate, the active toolbar and / or tray application are identified, and the inactive toolbar and / or tray application are closed.

[0014] According to a third aspect of the present invention, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method when executing the computer program.

[0015] According to a fourth aspect of the present invention, there is provided a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program implements the steps of the method when executed by a processor.

[0016] The RPA-based system and method for generating empirical materials for hospital smart management ratings provided in this application have the following beneficial effects: 1. High efficiency: Compared with the traditional method of obtaining materials by manual operation, the use of RPA technology can not only significantly improve the speed and accuracy of data processing, but also can continuously perform predefined tasks, reduce manual intervention, and greatly improve the efficiency of generating empirical materials for hospital smart management ratings; 2. Low error: By combining RPA technology and intelligent screenshot optimization technology, it is possible to accurately capture key information of system pages to reduce errors and omissions caused by manual operations, thereby improving the quality of screenshots and the efficiency of the automated rating process, and in conjunction with the coordinated use of the file generation module, it is possible to verify and quality control the empirical materials when generating materials to ensure that the final output text file of the empirical materials is accurate; 3. Optimize resource allocation: By adopting RPA technology, it is possible to realize automated operation of medical information systems to reduce dependence on external consulting and support services, thereby reducing the operating costs of hospitals, and can also identify human resources for users' current or upcoming operations. The system page resources are freed from the tedious data processing work, reducing the workload of medical staff. In addition, by setting the automatic closing toolbar and tray application module, based on the intelligent resource management mechanism, it can automatically recognize the face and operation step sequence information to ensure that business processing and screenshots are only performed in the active toolbar and / or tray application, so as to optimize the working environment of the medical information system, thereby improving the efficiency and accuracy of the hospital smart management rating; 4. High data quality: By combining RPA technology, AI technology and multimodal data fusion technology, the scattered data in the medical information system can be automatically collected and integrated to ensure the consistency and accuracy of the data, thereby improving the data quality of the generated empirical material text file; 5. Support continuous improvement: By setting the rating indicator dynamic adjustment sub-module, the preset rating indicators and corresponding weight values ​​can be dynamically adjusted according to the feedback information of the hospital smart management rating and the latest rating indicators related to the hospital smart management rating, so as to realize the dynamic adjustment of the content of the empirical material text file, and provide support for the continuous improvement of the hospital smart management rating work. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a system principle block diagram of the present invention.

[0018] Figure 2 This is a system principle block diagram of the user operation data acquisition module of the present invention.

[0019] Figure 3This is a system principle block diagram of the hospital smart management rating index setting module of the present invention.

[0020] Figure 4 This is a system principle block diagram of the automatic screenshot capturing module of the present invention.

[0021] Figure 5 This is a system principle block diagram of the material collection module of the present invention.

[0022] Figure 6 This is a system principle block diagram of the file generation module of the present invention.

[0023] Figure 7 It is the overall flow chart of the present invention.

[0024] Figure 8 Schematic diagram of an electronic device of the present invention. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below through specific implementation methods in conjunction with the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0026] Embodiment 1

[0027] Reference Figure 1-6 , an RPA-based hospital smart management rating empirical material generation system 100, the system includes a user operation data acquisition module 110, a hospital smart management rating indicator setting module 120, an automatic screenshot capture module 130, a material collection module 140, a file generation module 150 and an automatic toolbar and tray application closing module 160.

[0028] In some embodiments, refer to the attached Figure 2 The user operation data acquisition module 110 is used to capture user operation step data related to the hospital smart management rating in the medical information system based on RPA technology, AI technology and multimodal data fusion technology, and identify the system pages contained in the user operation step data and the key information contained in the system pages.

[0029] Furthermore, the user operation data acquisition module 110 includes a system initialization and configuration submodule 1110, which is used for the user to input configuration information related to the hospital smart management rating through a graphical user interface, and the configuration information includes the login credential information of the medical information system and the operation step sequence information for monitoring the user to complete the target task; the user operation capture submodule 1120 is used to automatically log in to the medical information system and monitor the user's operation steps to complete the target task based on the login credential information of the medical information system based on RPA technology, and capture the user operation step data related to the hospital smart management rating and the context environment information of the corresponding user operation steps based on multimodal data fusion technology; the AI ​​automatic recognition and recording submodule 1130 is used to identify the system pages and key information contained in the user operation step data based on the trained user behavior recognition AI model and natural language processing technology, and store the system page, the key information contained in the system page, the timestamp of the corresponding user operation step, and the context environment information of the corresponding user operation step in the database; the intelligent learning and optimization submodule 1140 is used to build and train the user behavior recognition AI model to obtain the trained user behavior recognition AI model.

[0030] Furthermore, after the system is started, the user inputs necessary configuration information through the graphical user interface of the system initialization and configuration submodule 1110. The configuration information includes login credential information of the medical information system and operation step sequence information for monitoring the user to complete the target task, for example: logging into the financial asset management system, navigating to a target module of the financial asset management system, querying a target data, and other operation step sequence information. The input of these operation step sequence information enables the system to accurately identify and monitor key operation processes, and then automatically capture user operation step data that is closely related to the hospital's smart management rating.

[0031] Furthermore, by setting up a user operation capture submodule 1120 and adopting RPA technology, according to the login credential information of the medical information system, the system can automatically log in to the medical information system and monitor the operation steps of the user to complete the target task, such as mouse clicks and keyboard input, so as to realize real-time capture of the user's operation steps and behaviors in the medical information system. Among them, the medical information system includes medical care management system, human resources management system, financial asset management system, equipment and facility management system, drug and consumables management system, operation management system, operation guarantee management system, teaching and scientific research management system, office management system, basic and security system.

[0032] In some embodiments, the user operation capture submodule 1120 also includes: a context perception and multimodal data processing submodule, which is used to obtain the user's operation step sequence information in the medical information system through log analysis and behavior tracking technology, and based on image processing technology, according to the operation step sequence information, capture the system page during the user's operation steps and the text information in the corresponding system page, and parse the text information through natural language processing technology to obtain the user's operation step intention information and the corresponding user operation step context information, analyze the system page, the user's operation intention information and the corresponding user operation step context information, and obtain the user operation step data related to the hospital smart management rating.

[0033] Furthermore, the specific implementation process of capturing user operation step data includes: first, through log analysis and behavior tracking technology, the context perception and multimodal data processing sub-module is used to collect the user's operation step sequence information in the medical information system, such as mouse clicks, keyboard input and browsing operation step sequence information; then, based on the operation step sequence information, image processing technology is used to capture the system page during user operation and extract text information in the system page; then, through natural language processing technology, the text information in the system page is parsed to identify the user's operation intention and the context environment information of the user's operation steps; finally, by combining the system page, text information and the context environment information of the user's operation steps, such as the time, frequency and sequence of the operation, a comprehensive analysis is performed to capture complete user operation step data, thereby ensuring the integrity and accuracy of the information.

[0034] Furthermore, by setting up an AI automatic identification and recording submodule 1130, using the trained user behavior recognition AI model and natural language processing technology, the system pages and their key information during user operations, such as meeting vouchers, purchase orders, reimbursement documents, etc., are automatically identified and extracted, and these system pages and their key information are added with the timestamp of the operation and the contextual environment information of the user operation steps and stored in the database, so as to provide accurate basic data for subsequent hospital smart management rating and analysis. This process can ensure the effective capture and recording of key information, and at the same time, optimize the working environment of the medical information system and the preparation process of rating materials.

[0035] Furthermore, by setting up an intelligent learning and optimization submodule 1140, a user behavior recognition AI model is constructed using machine learning algorithms such as random forests or neural networks. The machine learning algorithm can be flexibly selected according to actual usage conditions and is no longer limited here, as long as the purpose of identifying the operation steps of different users can be achieved; during the training process, multiple user operation step data are collected, and each user operation step data is labeled to obtain labeled data containing users completing different tasks. Based on the labeled data containing users completing different tasks, a data set is constructed, and the data set is randomly divided into a training set and a verification set in a ratio of 7:3. The training set and the verification set are input into the user behavior recognition AI model for training and verification, and the training process and hyperparameters, such as the depth of the decision tree or the weight of the neural network, are adjusted to optimize the recognition accuracy and obtain the trained user behavior recognition AI model; in addition, the system also continuously fine-tunes the user behavior recognition AI model by continuously learning the user's operation feedback so that it can adapt to changes in user behavior, thereby improving the recognition accuracy of user operation steps.

[0036] In some embodiments, refer to the attached Figure 3 The hospital smart management rating index setting module 120 is used to obtain rating indicators related to the hospital smart management rating, and based on RPA technology, associate the rating indicators with user operation step data to generate a comparison table of rating indicators and system pages.

[0037] Furthermore, the hospital smart management rating index setting module 120 includes a rating index data integration submodule 1210, which is used to obtain initial rating indicators related to hospital smart management rating from the website of the National Health Commission; a rating index parsing and mapping submodule 1220, which is used to form a mapping relationship between the initial rating indicators and the key information based on the preset mapping rules between the indicators and the key information; a user configuration and customization submodule 1230, which is used to select preset rating indicators from the initial rating indicators according to the target rating requirements of the hospital smart management rating, and assign corresponding weight values ​​to the preset rating indicators; a rating index dynamic adjustment submodule 1240, which is used to dynamically adjust the preset rating indicators and the corresponding weight values ​​according to the feedback information of the hospital smart management rating and the latest rating indicators related to the hospital smart management rating; an intelligent association and comparison table generation submodule 1250, which is used to associate the preset rating indicators with the user operation step data based on the RPA technology, and generate a comparison table of the rating indicators and the system page; a rating index review and release submodule 1260, which is used to review the comparison table of the rating indicators and the system page and the preset rating indicators, and release the preset rating indicators after the review is passed.

[0038] Furthermore, by setting up the rating index data integration submodule 1210, the system can obtain initial rating indexes related to the hospital smart management rating from the website of the National Health Commission. These initial rating indexes are integrated into the system to provide a basis for subsequent evaluation work.

[0039] Furthermore, by setting up the rating indicator parsing and mapping submodule 1220, the initial rating indicators can be converted into specific data query requirements. Through the preset mapping relationship between the indicators and key information, the key information on the system pages such as accounting vouchers, purchase orders, and reimbursement documents can be accurately located. Key information such as the voucher debit and credit amounts, dates, supplier information, etc., can be automatically extracted in the future, and based on this, the corresponding screenshots of empirical materials can be obtained, and then the key information empirical materials can be obtained, thereby supporting the visual proof of smart management ratings. This module can ensure that the rating indicators form a corresponding mapping relationship with the actual operating data of the hospital, laying the foundation for automated ratings.

[0040] Furthermore, by setting the user configuration and customization sub-module 1230, the user can configure the target rating requirements of the hospital's smart management rating through the graphical user interface, including selecting preset rating indicators from the initial rating indicators and setting corresponding weights. The system supports users to customize target rating requirements according to the specific circumstances of the hospital to adapt to different management goals.

[0041] Furthermore, by setting up a rating indicator dynamic adjustment submodule 1250 and utilizing RPA technology, the rating indicators can be automatically associated with the user operation step data recorded in the user operation capture submodule 1120, and a comparison table of rating indicators and system pages can be intelligently generated to provide accurate data support for automated rating.

[0042] Furthermore, by setting up a rating indicator dynamic adjustment submodule 1240, the preset rating indicators and corresponding weight values ​​can be dynamically adjusted according to the feedback information of the hospital smart management rating and the latest rating indicators related to the hospital smart management rating, such as adding, deleting or modifying the preset rating indicators and setting, increasing or decreasing the weight values ​​of the preset rating indicators, so as to ensure the timeliness and accuracy of the rating results.

[0043] Furthermore, by setting up a rating indicator review and release submodule 1260, the generated rating indicators are reviewed and processed with the comparison table of the system page and the preset rating indicators to ensure that they meet the actual situation and rating requirements of the hospital smart management rating. After the review is passed, the preset rating indicators are officially announced and used for the rating work of the hospital smart management.

[0044] In some embodiments, refer to the attached Figure 4The automatic screenshot capture module 130 is used to identify and determine the target rating indicators that need to be screenshotted according to the preset rating indicators of the hospital smart management rating, locate and match the system pages associated with the target rating indicators based on the comparison table of the rating indicators and the system pages, and based on the intelligent screenshot optimization technology, take screenshots of the key information contained in the associated system pages to obtain screenshot image data, and annotate the screenshot image data by type to obtain screenshot image annotation data including the target rating indicators.

[0045] Furthermore, the automatic screenshot capture module 130 includes a rating indicator identification and locking submodule 1310, which is used to identify and determine the target rating indicator that needs to be screenshotted according to the preset rating indicator of the hospital smart management rating; a system page comparison table matching submodule 1320, which is used to locate and match the system page associated with the target rating indicator according to the comparison table of the rating indicator and the system page, and identify and determine the system page that needs to be screenshotted; an automatic login and navigation submodule 1330, which is used to automatically log in to the medical information system based on the login credential information of the medical information system based on the RPA technology, and navigate to the corresponding system page according to the comparison table of the rating indicator and the system page; and an intelligent screenshot execution submodule 13 40, used to identify the attribute information of the corresponding system page after navigating to the corresponding system page, analyze the page elements of the corresponding system page based on the attribute information of the corresponding system page, identify at least one area range containing key information, automatically adjust the resolution and timing of the screenshot based on the area range containing the key information according to the content complexity of the corresponding system page, take a screenshot of the area range containing the key information, and obtain at least one screenshot image data; the screenshot data annotation and classification submodule 1350, used to annotate the screenshot image data by type, obtain screenshot image annotation data including target rating indicators, and classify and store the screenshot image annotation data according to the target rating indicators.

[0046] Furthermore, by setting the rating indicator identification and locking submodule 1310, the system can first identify and lock the specific target rating indicators that need to be screenshotted based on the preset rating indicators of the hospital smart management rating, so as to ensure that the goals of subsequent screenshot work are clear and closely related to the rating requirements.

[0047] Furthermore, by setting up a system page comparison table matching submodule 1320 and using the generated comparison table of rating indicators and system pages, the system can automatically match the system page corresponding to each target rating indicator. This module is the key to realizing automatic screenshots to ensure that the system page that needs to be screenshotted can be accurately found.

[0048] Furthermore, by setting up an automated login and navigation submodule 1330, RPA technology is used to automatically log in to the medical information system, and navigate to the corresponding system page according to the comparison table, thereby simulating the user's operation process to prepare the correct operating environment for the screenshot.

[0049] Furthermore, by setting up an intelligent screenshot execution submodule 1340, after navigating to a corresponding system page, firstly, the attribute information of a user entering a certain system page is identified, such as an accounting voucher or a purchase order page; secondly, based on the attribute information of the system page, the page elements of the system page are analyzed to identify the area containing key information; then, according to the content complexity of the corresponding system page, the resolution and timing of the screenshot are automatically adjusted, such as taking a screenshot immediately after the system page is loaded to avoid capturing incomplete information being loaded; when the content complexity of the system page reaches a preset content complexity threshold, the resolution of the screenshot can be increased to avoid the problem of unclear screenshots. In addition, the content complexity of the system page is determined by the number of characters on the system page; at the same time, the module can also intelligently select the screenshot range according to the importance of the system page and the distribution of data points to ensure that key information is not missed. It should be noted that by analyzing the page elements of the system page, if the system page contains multiple key information, at least one area range containing the key information will be identified, and a regional screenshot will be performed for each area range to ensure that each part is clearly visible; through intelligent screenshot optimization technology, key information can be captured more accurately, errors and omissions caused by manual operations can be reduced, and the quality of screenshots and the efficiency of the automated rating process can be improved.

[0050] Furthermore, by setting the screenshot data annotation and classification submodule 1350, the screenshot image data is annotated by type, including adding target rating indicators and descriptions, and classifying and storing the screenshot image annotation data according to the target rating indicators, which is helpful for subsequent rating analysis and material organization.

[0051] In some embodiments, refer to the attached Figure 5 The material collection module 140 is used to obtain the proof material data related to the screenshot image annotation data, wherein the proof material data is the business data related to the screenshot image annotation data, and classify the screenshot image annotation data and the proof material data according to the data type and the target rating index to obtain a plurality of classified data, extract the classified data to obtain the extracted data, archive the extracted data according to the target rating requirements, the target rating index and the data type, and generate index information and directory information.

[0052] Furthermore, the material collection module 140 includes a material identification and classification submodule 1410, which is used to obtain the proof material data related to the screenshot image annotation data, classify the screenshot image annotation data and the proof material data according to the target rating index, and obtain the first classification data under the target rating index classification, and classify the first classification data according to the data type to obtain the second classification data corresponding to each data type under each target rating index; the data extraction and organization submodule 1420 is used to extract data from the second classification data to obtain extracted data, and the extracted data includes the key information extracted from the screenshot image annotation data in the second classification data and the proof information related to the key information extracted from the proof material data in the second classification data, and the extracted data is structured format data; the material archiving and indexing submodule 1430 is used to archive the extracted data according to the target rating requirements, target rating indicators and data types, and generate index information and directory information.

[0053] Furthermore, by setting up a material identification and classification submodule 1410, the screenshot image annotation data and the supporting material data can be identified and acquired, and by utilizing artificial intelligence technology, the identified screenshot image annotation data and the supporting material data can be classified according to the target rating indicators and data types, wherein data types such as text, pictures, etc. are automatically classified to obtain the second classification data corresponding to each data type under each target rating indicator.

[0054] Furthermore, by setting up a data extraction and organization submodule 1420, key information and proof information are automatically extracted for the second classified data after identification and classification, such as extracting key information in text format from the screenshot image annotation data. The extracted information will be organized into a structured format for subsequent processing and analysis.

[0055] Furthermore, by setting up the material filing and indexing submodule 1430, the extracted materials are filed into corresponding directory structures according to the target rating requirements, target rating indicators and data types. At the same time, indexes and directories are generated so that users can quickly retrieve and access specific materials.

[0056] In some embodiments, refer to the attached Figure 6 The file generation module 150 is used to create file templates in various formats that meet the requirements of hospital smart management rating. The extracted data is matched with the predefined placeholders in the file template by using the template engine technology to obtain an automatically filled text file. The text file is styled and formatted based on the formatting tool to obtain an initial empirical material text file that meets the target rating requirements. The initial empirical material text file is quality verified to obtain a target empirical material text file.

[0057] Furthermore, the file generation module 150 includes a template preparation and customization submodule 1510, which is used to create file templates in various formats that meet the requirements of the hospital smart management rating; a data binding and automatic filling submodule 1520, which is used to match the extracted data with the predefined placeholders in the file template based on the template engine technology according to the target rating requirements, target rating indicators and data types, to obtain an automatically filled text file, wherein the target rating requirements, target rating indicators and data types correspond to different types of placeholders respectively; a formatting and typesetting submodule 1530, which is used to perform style and typesetting processing on the text file based on the formatting tool to obtain an initial empirical material text file that meets the target rating requirements; a data verification and quality control submodule 1540, which is used to obtain detailed information related to the hospital smart management rating, and to perform quality verification on the initial empirical material text file based on the detailed information to obtain a target empirical material text file; a file preservation and version management submodule 1550, which is used to store and version manage the target empirical material text file.

[0058] Furthermore, by setting up the template preparation and customization submodule 1510, before the system starts to be used, first prepare or customize various format file templates that meet the hospital smart management rating requirements. These file templates include but are not limited to Word documents, PDF files, Excel spreadsheets, etc., to adapt to different submission and archiving requirements, and ensure that these text templates contain all necessary rating items and format requirements.

[0059] Furthermore, by setting up a data binding and automatic filling submodule 1520 and utilizing template engine technology, the extracted data is matched with the predefined placeholders in the file template according to the target rating requirements, target rating indicators and data types. For example, key information and certification information in system pages such as accounting vouchers, purchase orders, and reimbursement documents are automatically bound to the placeholders in the file template. Through the template engine technology, predefined tags (placeholders) in the text template can be identified, and the actual data can be intelligently matched to the correct location according to the content type and rating requirements. For example, if a placeholder in the file template is used to refer to the purchase order number, the system will identify this location and automatically fill in the extracted purchase order number data. This automated data processing and filling method not only improves the efficiency of report generation, but also reduces human errors, ensuring the accuracy and consistency of the report content.

[0060] Furthermore, by setting the formatting and typesetting submodule 1530, the system's built-in formatting tools are used to perform style and typesetting processing on the text file, such as setting font style, color, table borders, chart style, etc., to ensure the professionalism and readability of the file, and obtain an initial empirical material text file that meets the target rating requirements.

[0061] Furthermore, by setting up a data verification and quality control submodule 1540, during the file generation process, the system executes a file verification process, checks the detailed information related to the hospital smart management rating, and checks whether the data meets the rating requirements. These detailed information comes from the specific guidelines and standards issued by the National Health Commission. By checking the data integrity, it is ensured that the collected information is comprehensive and complete; among them, the logical consistency analysis ensures that the relationship between the data is reasonable, such as the matching of dates and amounts; verifies the correctness of the data format, including text alignment, number format and unit usage, to ensure the professionalism and accuracy of the file content and presentation.

[0062] Furthermore, by setting up the file preservation and version management submodule 1550, after verification, the target empirical material text file is stored in a secure file management system, and version management is implemented to record every modification and update of the file for easy tracking and backtracking.

[0063] In some embodiments, the automatic toolbar and tray application closing module 160 is used to identify the system page and operation step sequence information that the user is currently or about to operate based on the user operation step data, and to identify the active toolbar and / or tray application based on the system page and operation step sequence information that the user is currently or about to operate, and to close the inactive toolbar and / or tray application.

[0064] Furthermore, by setting up the automatic closing toolbar and tray application module 160, through context-aware technology, it is possible to understand the user's current task and operation process, predict the user's upcoming operation, and thus determine which toolbars and / or tray applications are active; in this process, inactive toolbars and / or tray applications will be intelligently closed to avoid interfering with the screenshot process and improve the system response speed; the judgment of the active state is not only based on the user's current direct interaction, but also takes into account the logical flow of the operation and the system prompts, ensuring that business processing and screenshot work are only performed in the active toolbar and / or tray application. This intelligent resource management mechanism optimizes the working environment of the hospital information system and improves the efficiency and accuracy of the hospital's smart management rating.

[0065] Embodiment 2

[0066] Based on the above-mentioned embodiment 1, this embodiment provides a method for generating empirical materials for hospital smart management rating based on RPA. Figure 7 , applied to the RPA-based hospital smart management rating empirical material generation system of Example 1, the method includes the following steps.

[0067] S1. Through the user operation data acquisition module based on RPA technology, AI technology and multimodal data fusion technology, in the medical information system, capture the user operation step data related to the hospital smart management rating, and identify the system pages contained in the user operation step data and the key information contained in the system pages.

[0068] S2. Obtain rating indicators related to hospital smart management ratings through the hospital smart management rating indicator setting module. Based on RPA technology, associate the rating indicators with user operation step data to generate a comparison table of rating indicators and system pages.

[0069] S3. Through the automatic screenshot capture module, according to the preset rating indicators of the hospital smart management rating, identify and determine the target rating indicators that need to be screenshotted, based on the comparison table of rating indicators and system pages, locate and match the system pages associated with the target rating indicators, and based on the intelligent screenshot optimization technology, take screenshots of the key information contained in the associated system pages to obtain screenshot image data, and annotate the screenshot image data by type to obtain screenshot image annotation data including the target rating indicators.

[0070] S4. Acquire the proof material data related to the screenshot image annotation data through the material collection module, wherein the proof material data is the business data related to the screenshot image annotation data; classify the screenshot image annotation data and the proof material data according to the data type and the target rating index to obtain a plurality of classified data; extract the classified data to obtain extracted data; archive the extracted data according to the target rating requirements, the target rating index and the data type, and generate index information and directory information.

[0071] S5. Create file templates in various formats that meet the hospital smart management rating requirements through the file generation module. Use template engine technology to match the extracted data with the predefined placeholders in the file template to obtain an automatically filled text file. Based on the formatting tool, perform style and typesetting processing on the text file to obtain an initial empirical material text file that meets the target rating requirements. Perform quality verification on the initial empirical material text file to obtain the target empirical material text file.

[0072] S6. The automatic toolbar and tray application closing module identifies the system page and operation step sequence information that the user is currently or about to operate based on the user operation step data, and identifies the active toolbar and / or tray application based on the system page and operation step sequence information that the user is currently or about to operate, and closes the inactive toolbar and / or tray application.

[0073] Embodiment 3

[0074] This embodiment further provides an electronic device based on the above embodiment 1. Figure 8 , Figure 8 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0075] like Figure 8 As shown, the electronic device may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 to a random access memory (RAM) 303. Various programs and data required for the operation of the electronic device are also stored in the RAM 303. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0076] Typically, the following devices may be connected to the I / O interface 305: an input device 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, etc., an output device 307 including, for example, a liquid crystal display (LCD), a speaker, etc., a storage device 308 including, for example, a magnetic tape, a hard disk, etc., and a communication device 309. The communication device 309 may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Figure 8 An electronic device having various devices is shown, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 8 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0077] In particular, according to some embodiments of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, some embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from the network through the communication device 309, or installed from the storage device 308, or installed from the ROM 302. When the computer program is executed by the processing device 301, the above-mentioned functions defined in the method of some embodiments of the present disclosure are executed.

[0078] Embodiment 4

[0079] Based on the above-mentioned embodiment 1, this embodiment further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the above-mentioned method are implemented.

[0080] It should be noted that the computer-readable medium mentioned above in some embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In some embodiments of the present disclosure, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device. In some embodiments of the present disclosure, the computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer readable signal media may also be any computer readable medium other than computer readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0081] In this embodiment, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperTextTransferProtocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an adhoc peer-to-peer network), as well as any currently known or future developed network.

[0082] The computer-readable medium may be included in the device, or may exist independently without being installed in the electronic device. The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: obtains training data, converts the training data into initial data; determines an initial rule base based on the initial data, and optimizes the parameters of the initial rule base to obtain a target rule base; calculates the rules in the target rule base according to a preset activation weight calculation formula to obtain activation weights; determines abnormal information according to the test data and the activation weights.

[0083] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0084] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0085] The units described in some embodiments of the present disclosure may be implemented by software or hardware. The units described may also be provided in a processor, for example, may be described as: a processor including a data acquisition unit, a rule determination unit, a weight calculation unit, and an anomaly determination unit. The names of these units do not, in some cases, constitute limitations on the units themselves, for example, the data acquisition unit may also be described as a "unit for acquiring training data".

[0086] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that may be used include, without limitation, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), etc.

[0087] Obviously, it should be understood by those skilled in the art that the above-mentioned various steps of the present invention can be performed in a manner different from the present invention, and the simulation method and experimental equipment include but are not limited to the above description. The above-mentioned various steps of the present invention can be performed in an order different from that here in some cases, and the steps shown or described above can be performed separately. Therefore, the present invention is not limited to any specific combination of hardware and software.

[0088] The above contents are further detailed descriptions of the present invention in combination with specific implementation methods, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the scope of protection of the present invention.

Claims

1. The RPA-based hospital smart management rating evidence material generation system is characterized by: include: The user operation data acquisition module is used to capture user operation step data related to the hospital smart management rating in the medical information system based on RPA technology, AI technology and multimodal data fusion technology, and identify the system pages contained in the user operation step data and the key information contained in the system pages; The hospital smart management rating index setting module is used to obtain rating indicators related to hospital smart management ratings. Based on RPA technology, the rating indicators are associated with user operation step data to generate a comparison table of rating indicators and system pages. The automatic screenshot capture module is used to identify and determine the target rating indicators that need to be screenshotted according to the preset rating indicators of the hospital smart management rating, locate and match the system pages associated with the target rating indicators based on the comparison table of the rating indicators and the system pages, and based on the intelligent screenshot optimization technology, take screenshots of the key information contained in the associated system pages to obtain screenshot image data, and annotate the screenshot image data by type to obtain screenshot image annotation data including the target rating indicators; A material collection module is used to obtain the evidence material data related to the screenshot image annotation data, wherein the evidence material data is the business data related to the screenshot image annotation data, classify the screenshot image annotation data and the evidence material data according to the data type and the target rating index to obtain a plurality of classified data, extract the classified data to obtain the extracted data, archive the extracted data according to the target rating requirements, the target rating index and the data type, and generate index information and directory information; The file generation module is used to create file templates in various formats that meet the requirements of hospital smart management rating. The template engine technology is used to match the extracted data with the predefined placeholders in the file template to obtain an automatically filled text file. Based on the formatting tool, the text file is styled and formatted to obtain an initial empirical material text file that meets the target rating requirements. The initial empirical material text file is quality verified to obtain a target empirical material text file. The module for automatically closing toolbars and tray applications is used to identify the system page and operation step sequence information that the user is currently or about to operate based on the user's operation step data, and to identify active toolbars and / or tray applications based on the system page and operation step sequence information that the user is currently or about to operate, and to close inactive toolbars and / or tray applications.

2. The RPA-based hospital smart management rating empirical material generation system according to claim 1 is characterized in that: The user operation data acquisition module includes: The system initialization and configuration submodule is used for users to input configuration information related to the hospital smart management rating through a graphical user interface, wherein the configuration information includes login credential information of the medical information system and sequence information of operation steps for monitoring users to complete target tasks; the medical information system includes a medical care management system, a human resources management system, a financial asset management system, an equipment and facility management system, a drug and consumables management system, an operation management system, an operation guarantee management system, a teaching and scientific research management system, an office management system, and a basic and security system; The user operation capture submodule is used to automatically log in to the medical information system and monitor the operation steps of users to complete the target tasks based on the login credential information of the medical information system based on RPA technology. Based on multimodal data fusion technology, it captures the user operation step data related to the hospital smart management rating and the contextual environment information of the corresponding user operation steps; The AI ​​automatic identification and recording submodule is used to identify the system pages and key information contained in the user operation step data based on the trained user behavior identification AI model and natural language processing technology, and store the system pages, key information contained in the system pages, the timestamps corresponding to the user operation steps, and the context information corresponding to the user operation steps in the database; The intelligent learning and optimization submodule is used to build and train the user behavior recognition AI model to obtain the trained user behavior recognition AI model.

3. The RPA-based hospital smart management rating empirical material generation system according to claim 2 is characterized in that: The user operation capture submodule also includes: The context-awareness and multimodal data processing submodule is used to obtain the user's operation step sequence information in the medical information system through log analysis and behavior tracking technology, and based on image processing technology, capture the system page during the user's operation steps and the text information in the corresponding system page according to the operation step sequence information, and parse the text information through natural language processing technology to obtain the user's operation step intention information and the corresponding user operation step context information, analyze the system page, user operation intention information and the corresponding user operation step context information, and obtain user operation step data related to the hospital smart management rating.

4. The RPA-based hospital smart management rating empirical material generation system according to claim 1 is characterized in that: The hospital smart management rating index setting module includes: The rating indicator data integration submodule is used to obtain initial rating indicators related to hospital smart management rating from the website of the National Health Commission; The rating indicator parsing and mapping submodule is used to form a mapping relationship between the initial rating indicators and the key information based on the preset mapping rules between the indicators and the key information; The user configuration and customization submodule is used to select preset rating indicators from the initial rating indicators according to the target rating requirements of the hospital smart management rating, and assign corresponding weight values ​​to the preset rating indicators; The rating index dynamic adjustment submodule is used to dynamically adjust the preset rating index and the corresponding weight value according to the feedback information of the hospital smart management rating and the latest rating index related to the hospital smart management rating; The intelligent association and comparison table generation submodule is used to associate the preset rating indicators with the user operation step data based on RPA technology, and generate a comparison table of the rating indicators and system pages; The rating indicator review and release submodule is used to review the comparison table of rating indicators and system pages and preset rating indicators, and release the preset rating indicators after the review is passed.

5. The RPA-based hospital smart management rating empirical material generation system according to claim 4 is characterized in that: The automatic screenshot capture module comprises: The rating indicator identification and locking submodule is used to identify and determine the target rating indicators that need to be screenshotted based on the preset rating indicators of the hospital smart management rating; The system page comparison table matching submodule is used to locate and match the system pages associated with the target rating indicator according to the comparison table between the rating indicator and the system page, and identify and determine the system page that needs to be screenshotted; The automated login and navigation submodule is used to automatically log into the medical information system based on the login credential information of the medical information system based on RPA technology, and navigate to the corresponding system page based on the comparison table of rating indicators and system pages; The intelligent screenshot execution submodule is used to identify the attribute information of the corresponding system page after navigating to the corresponding system page, analyze the page elements of the corresponding system page based on the attribute information of the corresponding system page, identify at least one area range containing key information, automatically adjust the resolution and timing of the screenshot based on the area range containing the key information and according to the content complexity of the corresponding system page, and perform screenshot processing on the area range containing the key information to obtain at least one screenshot image data; The screenshot data annotation and classification submodule is used to perform type annotation on the screenshot image data, obtain the screenshot image annotation data including the target rating index, and classify and store the screenshot image annotation data according to the target rating index.

6. The RPA-based hospital smart management rating empirical material generation system according to claim 1 is characterized in that: The material collection module includes: The material identification and classification submodule is used to obtain the evidence material data related to the screenshot image annotation data, classify the screenshot image annotation data and the evidence material data according to the target rating index, obtain the first classification data classified under the target rating index, classify the first classification data according to the data type, and obtain the second classification data corresponding to each data type under each target rating index; A data extraction and arrangement submodule, configured to extract data from the second classified data to obtain extracted data, wherein the extracted data includes key information extracted from the screenshot image annotation data in the second classified data and proof information related to the key information extracted from the proof material data in the second classified data, and the extracted data is structured format data; The material archiving and indexing submodule is used to archive the extracted data according to the target rating requirements, target rating indicators and data types, and generate index information and directory information.

7. The RPA-based hospital smart management rating empirical material generation system according to claim 1 is characterized in that: The file generation module includes: Template preparation and customization submodule, used to create file templates in various formats that meet the hospital smart management rating requirements; The data binding and automatic filling submodule is used to match the extracted data with the predefined placeholders in the file template based on the template engine technology according to the target rating requirements, target rating indicators and data types to obtain the automatically filled text file, wherein the target rating requirements, target rating indicators and data types correspond to different types of placeholders respectively; The formatting and typesetting submodule is used to process the style and typesetting of text files based on formatting tools to obtain the initial empirical material text files that meet the target rating requirements; The data verification and quality control submodule is used to obtain detailed information related to the hospital smart management rating, and to perform quality verification on the initial empirical material text file based on the detailed information to obtain the target empirical material text file; The file preservation and version management submodule is used to store and version the target empirical material text files.

8. The method for generating empirical materials for hospital smart management rating based on RPA is characterized by: include: Through the user operation data acquisition module, based on RPA technology, AI technology and multimodal data fusion technology, the user operation step data related to the hospital smart management rating is captured in the medical information system, and the system pages contained in the user operation step data and the key information contained in the system pages are identified; The hospital smart management rating index setting module is used to obtain rating indexes related to the hospital smart management rating. Based on RPA technology, the rating indexes are associated with the user operation step data to generate a comparison table between the rating indexes and the system pages. Through the automatic screenshot capture module, according to the preset rating indicators of the hospital smart management rating, the target rating indicators that need to be screenshoted are identified and determined, and based on the comparison table of rating indicators and system pages, the system pages associated with the target rating indicators are positioned and matched, and based on the intelligent screenshot optimization technology, the key information contained in the associated system pages is screenshoted to obtain screenshot image data, and the screenshot image data is type-labeled to obtain screenshot image labeling data including the target rating indicators; Acquire the evidence material data related to the screenshot image annotation data through the material collection module, wherein the evidence material data is the business data related to the screenshot image annotation data, classify the screenshot image annotation data and the evidence material data according to the data type and the target rating index to obtain a plurality of classified data, extract the classified data to obtain extracted data, archive the extracted data according to the target rating requirements, the target rating index and the data type, and generate index information and directory information; Create file templates in various formats that meet the hospital smart management rating requirements through the file generation module. Use template engine technology to match the extracted data with the predefined placeholders in the file template to obtain an automatically filled text file. Based on the formatting tool, perform style and typesetting processing on the text file to obtain an initial empirical material text file that meets the target rating requirements. Perform quality verification on the initial empirical material text file to obtain the target empirical material text file. The automatic toolbar and tray application closing module identifies the system page and operation step sequence information that the user is currently or about to operate based on the user's operation step data, and identifies the active toolbar and / or tray application based on the system page and operation step sequence information that the user is currently or about to operate, and closes the inactive toolbar and / or tray application.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to claim 8 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to claim 8 are implemented.

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