A data processing method and device based on RPA, equipment and medium

Abstract

The embodiment of the application provides a kind of based on RPA data processing method and device, equipment and medium, belong to artificial intelligence technical field.The method comprises: starting RPA robot, obtains business event single by calling first interface;Business event single is classified and identified by recommendation algorithm model, and fixed scheme is selected from fixed scheme knowledge base;Fixed scheme is fed back to corresponding processing object, and the modification parameter uploaded by processing object is obtained;Fixed scheme is assembled and filled according to modification parameter, and data modification scheme is obtained;Data modification processing is carried out to first database, and data signing single is obtained;Calling second interface carries out signing and reporting approval processing to data signing single, and approval result is obtained;When approval result is approval, data modification processing is carried out to second database, and production execution log is obtained.The embodiment of the application can improve the processing efficiency of data modification, and can be widely applied in artificial intelligence technical field.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, and in particular to a data processing method, apparatus, device and medium based on RPA. Background Technology

[0002] With the rapid development of computer technology, applications and systems are increasingly processing more and more data. Currently, data modification still requires manual operation, and the data processing workflow is fixed and cumbersome, resulting in low efficiency. Therefore, improving the efficiency of data modification has become an urgent technical problem to be solved. Summary of the Invention

[0003] The main objective of this application is to propose a data processing method, apparatus, device, and medium based on RPA, which aims to improve the efficiency of data modification processing.

[0004] To achieve the above objectives, a first aspect of this application proposes a data processing method based on RPA, the method comprising:

[0005] Start the RPA robot and call the first interface to obtain the business event order;

[0006] The business event order is classified and identified by a pre-trained recommendation algorithm model, and a fixed solution is selected from the fixed solution knowledge base based on the classification and identification results.

[0007] The fixed solution is fed back to the corresponding processing object, and the modified parameters uploaded by the processing object are obtained;

[0008] The fixed scheme is assembled and filled according to the modified parameters to obtain the data modification scheme;

[0009] The data modification process is performed on the first database according to the data modification scheme to obtain a data signing form;

[0010] The second interface is invoked to process the data submission form for approval, and the approval result is obtained.

[0011] When the approval result is "approved", the data in the second database is modified according to the data modification plan to obtain the production execution log.

[0012] In some embodiments, the step of modifying the data in the first database according to the data modification scheme to obtain a data sign-off form includes:

[0013] The first database is determined based on the data modification scheme;

[0014] The third interface is invoked to perform an entry check on the first database, and the check result is obtained.

[0015] Based on the inspection results, the first interface is invoked to modify the data in the first database, and a first data modification result is obtained.

[0016] The third interface is called to perform data verification processing on the first data modification result, and a data signature form is obtained.

[0017] In some embodiments, the step of calling the first interface to modify the data in the first database based on the inspection result to obtain a first data modification result includes:

[0018] Determine the data modification script based on the aforementioned data modification scheme;

[0019] Based on the inspection results, the first interface is invoked to execute the data modification script, perform data modification processing on the first database, and obtain the abnormal content of the data modification script;

[0020] When the abnormal content of the data modification script is empty, the first data modification result is obtained;

[0021] Alternatively, when the abnormal content of the data modification script is not empty, perform anomaly analysis on the abnormal content, feed back the anomaly analysis results to the corresponding processing object, and obtain the modification scheme uploaded by the processing object.

[0022] Update the data modification script according to the modification scheme, return to the step of calling the first interface to execute the data modification script on the first database according to the inspection result, and obtain the abnormal content of the data modification script.

[0023] In some embodiments, the step of calling the third interface to perform data verification processing on the first data modification result to obtain a data signature form includes:

[0024] Determine the data verification script based on the data modification scheme;

[0025] Call the third interface to execute the data verification script to perform data verification processing on the first data modification result and obtain the data verification result;

[0026] When the data verification result is passed, the first interface is called to obtain the data signature form;

[0027] Alternatively, when the data verification result is unsuccessful, the business event order is stopped, and the data verification result is fed back to the corresponding processing object.

[0028] In some embodiments, the step of calling the second interface to perform approval processing on the data report and obtain an approval result includes:

[0029] Obtain the approval chain for the report;

[0030] The second interface is invoked to initiate a data signing and approval application for the data signing form, the data signing form is approved and processed according to the signing and approval chain, and the approval status of the data signing form is obtained.

[0031] If the approval status of the data report is "approved", obtain the approval result;

[0032] Alternatively, if the approval status of the data sign-off form is "not approved", the system will wait for a preset time and then return to the step of obtaining the approval status of the data sign-off form.

[0033] In some embodiments, when the approval result is "approved," the second database is modified according to the data modification scheme to obtain a production execution log, including:

[0034] When the approval result is "approved", the second database is determined according to the data modification plan;

[0035] The first interface is called to modify the data in the second database, and the modified data result is obtained.

[0036] The third interface is called to perform data verification on the second data modification result, and after the verification is successful, the fourth interface is called to obtain the production execution log.

[0037] In some embodiments, the method further includes:

[0038] Obtain the data modification scheme and the production execution log;

[0039] The system calls the first interface to upload the data modification scheme and the production execution log to the system, and notifies the corresponding processing object of the upload result.

[0040] To achieve the above objectives, a second aspect of this application provides a data processing apparatus based on RPA, the apparatus comprising:

[0041] The first module is used to start the RPA robot and call the first interface to obtain the business event order.

[0042] The second module is used to classify and identify the business event order through a pre-trained recommendation algorithm model, and select a fixed solution from the fixed solution knowledge base based on the classification and identification results.

[0043] The third module is used to feed back the fixed scheme to the corresponding processing object and obtain the modified parameters uploaded by the processing object;

[0044] The fourth module is used to assemble and fill the fixed scheme according to the modified parameters to obtain the data modification scheme;

[0045] The fifth module is used to modify the data in the first database according to the data modification scheme to obtain a data signature form;

[0046] The sixth module is used to call the second interface to process the data report for approval and obtain the approval result;

[0047] The seventh module is used to modify the data in the second database according to the data modification scheme when the approval result is "approved" to obtain the production execution log.

[0048] To achieve the above objectives, a third aspect of the present application provides an electronic device, the electronic device including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method described in the first aspect.

[0049] To achieve the above objectives, a fourth aspect of the present application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect.

[0050] This application proposes an RPA-based data processing method, apparatus, device, and medium. It involves activating an RPA robot to obtain business event orders by calling a first interface; classifying and identifying the business event orders using a pre-trained recommendation algorithm model; selecting a fixed solution from a fixed solution knowledge base based on the classification results; feeding the fixed solution back to the corresponding processing object and obtaining the modification parameters uploaded by the processing object. By using an RPA robot to identify business events, it effectively reduces user operation time and improves data modification processing efficiency. The fixed solution is then assembled and populated according to the modification parameters to obtain a data modification solution. Data modification processing is performed on a first database according to the data modification solution to obtain a data report; a second interface is called to process the data report for approval, obtaining an approval result; when the approval result is approved, data modification processing is performed on a second database according to the data modification solution to obtain a production execution log. This method reduces errors in manual processing through process automation, thereby lowering labor costs and improving data processing efficiency. Attached Figure Description

[0051] Figure 1 This is a flowchart of a data processing method based on RPA provided in an embodiment of this application;

[0052] Figure 2 yes Figure 1 The flowchart of step S105 in the process;

[0053] Figure 3 yes Figure 2 The flowchart of step S203 in the process;

[0054] Figure 4 yes Figure 2 The flowchart of step S204 in the process;

[0055] Figure 5 yes Figure 1 The flowchart of step S106 in the process;

[0056] Figure 6 yes Figure 1 The flowchart of step S107 in the process;

[0057] Figure 7 yes Figure 1 The flowchart following step S107.

[0058] Figure 8 This is a schematic diagram of the structure of a data processing device based on RPA provided in an embodiment of this application;

[0059] Figure 9 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0061] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0062] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0063] First, let's analyze some of the terms used in this application:

[0064] Artificial intelligence (AI) is a new branch of computer science that studies, develops, and applies theories, methods, technologies, and systems to simulate, extend, and expand human intelligence. It aims to understand the essence of intelligence and produce intelligent machines that can react in a way similar to human intelligence. Research in this field includes robotics, speech recognition, image recognition, natural language processing, and expert systems. AI can simulate the information processes of human consciousness and thought. Furthermore, AI utilizes digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceiving the environment, acquiring knowledge, and using that knowledge to achieve optimal results.

[0065] Robotic process automation (RPA): RPA refers to the development of process robots that can simulate human actions such as clicking, downloading, and data processing. Based on artificial intelligence and automation technology, they interact with existing user systems according to pre-recorded scripts and complete expected tasks, replacing humans in performing repetitive tasks and workflows through the user interface of one or more system tools on a computer, thereby improving efficiency.

[0066] Logistic regression (LR), also known as the log-odds model, is a classic machine learning classification model widely used in online applications due to its strong interpretability, simplicity of implementation, and high online efficiency. Logistic regression is simple, highly interpretable, and easy to implement, and is widely applied in machine learning, deep learning, recommender systems, advertising prediction, intelligent marketing, financial risk control, sociology, biology, economics, and other fields.

[0067] As described in the background section, in related technologies, manual modification of large amounts of data is prone to errors and consumes a significant amount of time, impacting data modification efficiency. When the volume of business to be processed increases, the data modification process becomes slow, business operations cannot respond quickly, resulting in a poor user experience.

[0068] Based on this, embodiments of this application provide a data processing method, apparatus, device, and medium based on RPA, aiming to improve the processing efficiency of data modification.

[0069] This application provides a data processing method, apparatus, device, and medium based on RPA, which will be specifically described through the following embodiments. First, the data processing method in the embodiments of this application is described.

[0070] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0071] Foundational technologies for artificial intelligence generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interactive systems, and mechatronics. AI software technologies mainly encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0072] This application provides an RPA-based data processing method, relating to the field of artificial intelligence technology. This RPA-based data processing method can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, etc.; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application implementing an RPA-based data processing method, but is not limited to the above forms.

[0073] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0074] Figure 1This is an optional flowchart of a data processing method based on RPA provided in an embodiment of this application. Figure 1 The method may include, but is not limited to, steps S101 to S107.

[0075] Step S101: Start the RPA robot and call the first interface to obtain the business event order;

[0076] Step S102: Classify and identify the business event order using a pre-trained recommendation algorithm model, and select a fixed solution from the fixed solution knowledge base based on the classification and identification results;

[0077] Step S103: Feed back the fixed scheme to the corresponding processing object and obtain the modified parameters uploaded by the processing object;

[0078] Step S104: Assemble and fill the fixed scheme according to the modified parameters to obtain the data modification scheme;

[0079] Step S105: Modify the data in the first database according to the data modification scheme to obtain a data signing form;

[0080] Step S106: Call the second interface to process the data report for approval and obtain the approval result;

[0081] Step S107: When the approval result is "approved", the second database is modified according to the data modification scheme to obtain the production execution log.

[0082] As shown in steps S101 to S107 of this embodiment, firstly, by starting the RPA robot and calling the first interface, a business event order is obtained. This allows for fully automated acquisition of business event orders through the RPA robot, reducing the time spent on manual conversion or system login. Then, this embodiment classifies and identifies the business event order using a pre-trained recommendation algorithm model. Based on the classification results, a fixed solution is selected from a fixed solution knowledge base. This allows for the establishment of a fixed solution knowledge base based on a fixed data modification process. Selecting a fixed solution from this knowledge base by identifying the business event order facilitates rapid identification of business events. Finally, in this embodiment, the fixed scheme is fed back to the corresponding processing object, and the modification parameters uploaded by the processing object are obtained; the fixed scheme is assembled and filled according to the modification parameters to obtain a data modification scheme; the data modification is performed on the first database according to the data modification scheme to obtain a data report; the second interface is called to perform approval processing on the data report to obtain an approval result; when the approval result is approved, the data modification is performed on the second database according to the data modification scheme to obtain a production execution log. This can automate the data modification process, reduce errors caused by manual data modification, and improve the efficiency of data modification.

[0083] In step S101 of some embodiments, the RPA robot is started and the first interface is called to obtain the business event order;

[0084] In this embodiment, an RPA robot is first activated, and a business event form is obtained by calling a first interface through the RPA robot. The RPA robot is a pre-configured computer program that can be applied to the business system to perform various operations, such as deleting data and calling interfaces. The first interface is the business event reporting system interface. By calling this interface, one can log in or connect to the business event reporting system and obtain the uploaded business event form. This business event form records a specific description of the business event, such as: "When Customer A applies for a claim, the system indicates that there are multiple customer numbers. Upon verification, there are two customer numbers under this customer's name, A1 and A2. Customer number A2 has no policy, and the policyholder cannot supplement maintenance information or report the merging of customer numbers. Therefore, the data modification request is made to the IT backend to delete customer number A2." This embodiment of the application obtains business event forms by calling the first interface through an RPA robot, which reduces the interaction process between the front-end and back-end and improves data processing efficiency.

[0085] In step S102 of some embodiments, the business event order is classified and identified by a pre-trained recommendation algorithm model, and a fixed scheme is selected from the fixed scheme knowledge base based on the classification and identification results.

[0086] In this embodiment, after the RPA robot acquires a business event ticket, it classifies and identifies the business event ticket using a pre-trained recommendation algorithm model. The specific training process is as follows: data from a pre-labeled training dataset is input into the initialized recommendation algorithm model, resulting in the model's output classification result, i.e., the recommendation algorithm classification result. The accuracy of the recommendation algorithm model's classification can be evaluated based on the classification result and the aforementioned pre-labeled tags, thereby updating the model's parameters. The recommendation algorithm model may include a logistic regression model, which classifies and identifies the business event ticket to obtain the classification result. Logistic regression, as a classic machine learning classification model, can classify business event tickets. This logistic regression model includes linear regression and logistic function models. It is conceivable that the recommendation algorithm model in this embodiment may also include a language recognition model, such as using a CRNN model to recognize text in the business event ticket, and then using a logistic regression model to classify the business event ticket to obtain the classification result. This application embodiment also establishes a fixed scheme knowledge base, which stores fixed schemes. Fixed schemes are used to integrate fixed data processing flows to obtain specific data processing solutions, including at least the database to be modified, an entry check script, a data modification script, and a data verification script. The entry check script performs entry checks on the database that the fixed scheme needs to connect to. The data modification script modifies the data in the database. The data verification script verifies whether the data has been successfully modified in the database. This application embodiment classifies and identifies business event orders, and selects the corresponding fixed scheme from the fixed scheme knowledge base based on the classification results.

[0087] In step S103 of some embodiments, the fixed scheme is fed back to the corresponding processing object, and the modified parameters uploaded by the processing object are obtained;

[0088] In this embodiment of the invention, the selected fixed solution is fed back to the corresponding processing object, which then uploads modified parameters according to the fixed solution. It is conceivable that each business event is associated with a corresponding processing object, which is the corresponding backend staff member. This association can be based on the business number of the business event or on the fixed solution; no specific limitation is made here. This embodiment of the invention displays the fixed solution in the backend staff member's processing system and can send it to the backend staff member's terminal via email or other means, thereby notifying the backend staff member to log in to the system for corresponding processing. The modified parameters uploaded by the processing object may include information such as the policy number, insurance number, case number, and data modification number.

[0089] In step S104 of some embodiments, the fixed scheme is assembled and filled according to the modified parameters to obtain the data modification scheme;

[0090] In this embodiment of the invention, a data modification scheme is obtained by assembling and filling a fixed scheme based on the modification parameters uploaded by the processing object. In one specific implementation, the fixed scheme is to delete a customer number, and the modification parameter uploaded by the processing object is the customer number to be deleted. This modification parameter is filled into the data modification script in the fixed scheme to obtain a complete data modification scheme, which can be used to modify data in the database. It is conceivable that the RPA robot will automatically supplement processing object information, event content information, the event order that needs to be modified, etc., to facilitate subsequent process traceability.

[0091] Please see Figure 2 In some embodiments, step S105 involves modifying the data in the first database according to the data modification scheme to obtain a data sign-off form, which may include, but is not limited to, steps S201 to S204.

[0092] Step S201: Determine the first database according to the data modification scheme;

[0093] Step S202: Call the third interface to perform an entry check on the first database and obtain the check result;

[0094] Step S203: Based on the inspection result, call the first interface to perform data modification processing on the first database to obtain the first data modification result;

[0095] Step S204: Call the third interface to perform data verification processing on the first data modification result to obtain a data signature form.

[0096] In this embodiment, a first database is first selected according to the data modification scheme. This first database is a pre-production database, which is also a backup database. Then, an entry check is performed on the first database by calling a third interface, which is a database execution platform interface. Specifically, the entry check script in the data modification scheme checks whether the connection to the correct database is correct. Next, based on the check result, the first interface is called to modify the data in the first database. Specifically, the data modification script is executed in the business reporting event system to modify the data in the first database, resulting in the first data modification result. Finally, the third interface is called to verify the first data modification result. Specifically, the data verification script is executed in the database execution platform to verify the modified data in the first database, checking whether the data modification was successful. If the verification is successful, a data sign-off form is obtained. This embodiment, by modifying data in the first database, reduces the possibility of errors interfering with the system due to direct data modification in the production database.

[0097] Please see Figure 3 In step S203 of some embodiments, the step of calling the first interface to modify the data of the first database according to the inspection result and obtaining the first data modification result may include, but is not limited to, steps S301 to S305.

[0098] Step S301: Determine the data modification script according to the data modification scheme;

[0099] Step S302: Based on the inspection results, call the first interface to execute the data modification script, perform data modification processing on the first database, and obtain the abnormal content of the data modification script;

[0100] Step S303: When the abnormal content of the data modification script is empty, the first data modification result is obtained;

[0101] Step S304, or, when the abnormal content of the data modification script is not empty, perform anomaly analysis on the abnormal content, feed back the anomaly analysis results to the corresponding processing object, and obtain the modification scheme uploaded by the processing object;

[0102] Step S305: Update the data modification script according to the modification scheme, return to the step of calling the first interface to execute the data modification script on the first database according to the inspection result, and obtain the abnormal content of the data modification script.

[0103] In this embodiment, a data modification script is first obtained from the data modification scheme. The script is an executable file written in a specific descriptive language according to a certain format, also known as a macro or batch file. In this embodiment, the data modification script is a pre-written executable file used to modify data in the database. Before modifying the database data, the database entry point needs to be checked. This can be done by obtaining a data verification script from the data modification scheme to check the database entry point and obtain the check result. When the check result is correct, the first interface is called to connect to the business reporting event system to execute the data modification script in the first database and modify the data in the first database. If errors or other problems occur during the execution of the data modification script, abnormal content will appear. This abnormal content is obtained by acquiring the data modification script. When the abnormal content of the data modification script is empty, the script execution is error-free, and the first data modification result is obtained. When the abnormal content of the data modification script is not empty, the script execution encounters an error. Anomaly analysis is performed on the abnormal content, and the anomaly analysis result is fed back to the corresponding processing object, and the modification scheme uploaded by the processing object is obtained. It is conceivable that if the RPA robot determines that the exception can be re-executed, it will automatically re-execute the script. If the exception persists after three consecutive executions, the processing object will be notified to intervene. Common exception types include: database selection error, parameter filling error, database table not existing, database field not existing, etc. In this embodiment, the RPA robot feeds back the exception content to the processing object for modification, which helps the processing object quickly identify relevant exception content and greatly reduces the exception handling operation time.

[0104] Please see Figure 4 In some embodiments, step S204, where the third interface is called to perform data verification processing on the first data modification result to obtain a data signature, may include, but is not limited to, steps S401 to S404.

[0105] Step S401: Determine the data verification script based on the data modification scheme;

[0106] Step S402: Call the third interface to execute the data verification script to perform data verification processing on the first data modification result and obtain the data verification result;

[0107] Step S403: When the data verification result is passed, the first interface is called to obtain the data signature form;

[0108] Step S404, or, when the data verification result is unsuccessful, stop the business event order and feed back the data verification result to the corresponding processing object.

[0109] In this embodiment, a data verification script is first obtained from the data modification scheme. The data verification script is then executed by calling a third interface to connect to the database execution platform and verify the first data modification result, confirming whether the data to be modified was successfully modified in the first database. If the verification passes, the RPA robot calls the first interface to connect to the business reporting event system to obtain a data report. This data report may include the reporting requirements submitted by the business: such as entering a change description, database type, executing user, database, uploading the production database data modification script, verification script, etc. If the verification fails, the RPA robot stops and closes the currently executing business event to prevent the program from continuing execution and feeds back the data verification result to the corresponding processing object. It is conceivable that this embodiment can notify the corresponding processing object via email or SMS to provide feedback. This embodiment verifies data modifications using the data verification script in the data modification scheme, ensuring that the data is correctly modified in the pre-production database, and feeds back any potential errors to the corresponding processing object via email notifications, thereby improving the accuracy of data modifications.

[0110] Please see Figure 5 In some embodiments, step S106, where the second interface is called to process the data report for approval and obtain the approval result, may include, but is not limited to, steps S501 to S504.

[0111] Step S501: Obtain the approval chain for the report;

[0112] Step S502: Call the second interface to initiate a data signing approval application for the data signing form, process the data signing form for approval according to the signing approval chain, and obtain the approval status of the data signing form;

[0113] Step S503: If the approval status of the data report is "approved", obtain the approval result;

[0114] Step S504, or, if the approval status of the data sign-off form is unapproved, wait for a preset time and then return to the step of obtaining the approval status of the data sign-off form.

[0115] In this embodiment, the approval chain is first obtained, which is a pre-set approval process. The RPA robot connects to the data signing system through a second interface to initiate a data signing approval application, and sends the data signing form to the corresponding approval object according to the pre-set approval chain. The RPA robot also periodically obtains the approval status of the data signing form. If the approval status of the data signing form is approved, the corresponding approval result is obtained; if the approval status of the data signing form is not approved, it will return to the step of obtaining the approval status of the data signing form after waiting for a preset time. It is conceivable that the preset time can be set independently according to the actual situation. When processing multiple data signing forms, if only one signing status is checked at a time, multiple processes will be started, thereby consuming server performance. However, in this embodiment, the RPA robot distinguishes the signing form type and signing number, and obtains the approval result of the data signing form with the approval status of approved at the same time. The data signing form without approval will continue to wait for the next polling. Periodically obtaining the approval status can improve server performance. If a data submission is rejected, the RPA robot determines that there is a problem with the submission, automatically closes it, and notifies the relevant processing object to analyze the data before resubmitting. This embodiment simplifies and streamlines the data processing approval process by setting a preset timeframe for obtaining the approval status of data submissions, thereby increasing the speed of data approval.

[0116] Please see Figure 6 In step S107 of some embodiments, when the approval result is approval passed, the data modification process is performed on the second database according to the data modification scheme to obtain the production execution log, including but not limited to steps S601 to S603.

[0117] Step S601: When the approval result is "approved", determine the second database according to the data modification scheme;

[0118] Step S602: Call the first interface to modify the data in the second database and obtain the second data modification result;

[0119] Step S603: Call the third interface to perform data verification processing on the second data modification result, and call the fourth interface after the verification is successful to obtain the production execution log.

[0120] In this embodiment, data modification requires initial modification and approval in the pre-production database before it can be performed in the production database. When the approval result for data modification in the first database is passed, data modification is performed in the second database according to the data modification plan. The second database is the production database, the first database is the pre-production database, and the first database is a backup database of the second database. The two databases store identical data; the first database is used for production or system operation, and the second database is used to back up the first database. This embodiment, by modifying data in the first database, simulates various possible scenarios when modifying data in the second database, and by verifying data in the first database, improves the accuracy of data modification.

[0121] In this embodiment, if the approval result is successful, a second database is selected according to the data modification plan. The RPA robot calls the first interface to connect to the business reporting event system to modify the data in the second database, obtaining the second data modification result. It is conceivable that the RPA robot modifies the data in the second database according to the data modification script in the data modification plan. If a modification exception occurs during the execution of the data modification script, the script will not be executed repeatedly; instead, it will be reported back to the corresponding processing object to avoid dirty data being generated in the production database due to execution problems. The RPA robot connects to the database execution platform through a third interface to perform data verification processing on the second data modification result, that is, to verify the second data modification result according to the data verification script in the data modification plan, ensuring successful data modification. After successful data verification, the RPA robot calls the fourth interface to connect to the log system and automatically exports the production execution log. The fourth interface is used to connect to the log system and generate execution logs to record the data modification process and information. In this embodiment, data modifications are only made in the production database after approval is obtained from modifications made in the pre-production database. This reduces the risk of system errors that are difficult to handle in a timely manner due to anomalies that may occur during data modification in the production database. Furthermore, the data modification process is automated through RPA robots, which improves the efficiency of data modification processing.

[0122] Please see Figure 7 In some embodiments, after step S107, the method may further include steps S701 to S702.

[0123] Step S701: Obtain the data modification scheme and the production execution log;

[0124] Step S702: Call the first interface to perform system upload processing on the data modification scheme and the production execution log, and notify the corresponding processing object of the upload result.

[0125] In this embodiment, after data modification is completed in the second database, the RPA robot obtains the data modification plan and production execution logs, calls the first interface to connect to the production reporting event system, and uploads the data modification plan and production execution logs to the production reporting event system for subsequent inspection of data modifications. Furthermore, the RPA robot notifies the corresponding processing object of the upload result. In this embodiment, the notification can be sent to the corresponding processing object via email, allowing the processing object to understand the specific data processing process and verify the data processing. This embodiment saves the scripts executed in the production database and related execution logs through the RPA robot's upload to the system, facilitating subsequent traceability of data modifications and improving the security of data modification; and by notifying the corresponding processing object via email, the processing object can be informed of the data modification status immediately, improving data processing efficiency.

[0126] In one specific embodiment, this application embodiment starts an RPA robot, calls a first interface to connect to the business reporting event system to obtain a business event form uploaded by the business end. This business event form is a specific business description, which in this embodiment can be summarized as deleting customer number A2. A pre-trained recommendation algorithm model is used to classify and identify the business event form. This recommendation algorithm model includes a logistic regression model. The logistic regression model is used to classify and identify the business event form, and the classification result is "delete customer number." A fixed solution for deleting the customer number is selected from the fixed solution knowledge base. The selected fixed solution is fed back to the corresponding processing object. The processing object checks the fixed solution, and if it is correct, it fills in the corresponding modification parameters. In this embodiment, the processing object fills in A2. The RPA robot obtains the modification parameters uploaded by the processing object and assembles and fills in the fixed solution according to the modification parameters. Specifically, the modification parameters are filled into the data modification script, and the database execution statements are filled in to obtain the final data modification solution. A first database can be selected based on the data modification solution. The first database is a backup database, and the corresponding second database is the database used in actual production applications. First, data is modified in the first database according to the data modification plan. The specific data modification process includes database entry checks, database data modification, and data verification. After the data verification is successful, a data approval form is generated. The data approval form is then connected to the approval system via a second interface for approval. Only after approval is granted is the data modified in the second database. This completes the data modification process and generates a production execution log, which records the data modification process and related information.

[0127] Please see Figure 8 This application also provides an RPA-based data processing apparatus that can implement the above-described RPA-based data processing method. The apparatus includes:

[0128] The first module is used to start the RPA robot and call the first interface to obtain the business event order.

[0129] The second module is used to classify and identify the business event order through a pre-trained recommendation algorithm model, and select a fixed solution from the fixed solution knowledge base based on the classification and identification results.

[0130] The third module is used to feed back the fixed scheme to the corresponding processing object and obtain the modified parameters uploaded by the processing object;

[0131] The fourth module is used to assemble and fill the fixed scheme according to the modified parameters to obtain the data modification scheme;

[0132] The fifth module is used to modify the data in the first database according to the data modification scheme to obtain a data signature form;

[0133] The sixth module is used to call the second interface to process the data report for approval and obtain the approval result;

[0134] The seventh module is used to modify the data in the second database according to the data modification scheme when the approval result is "approved" to obtain the production execution log.

[0135] The specific implementation of this RPA-based data processing device is basically the same as the specific implementation of the RPA-based data processing method described above, and will not be repeated here.

[0136] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the aforementioned data processing method based on an RPA robot. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0137] Please see Figure 9 , Figure 9 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes:

[0138] The processor 901 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to achieve the technical solutions provided in the embodiments of this application.

[0139] The memory 902 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 902 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 902 and is called and executed by the processor 901 according to an RPA-based data processing method of this application embodiment.

[0140] The input / output interface 903 is used to implement information input and output;

[0141] The communication interface 904 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0142] Bus 905 transmits information between various components of the device (e.g., processor 901, memory 902, input / output interface 903, and communication interface 904);

[0143] The processor 901, memory 902, input / output interface 903, and communication interface 904 are connected to each other within the device via bus 905.

[0144] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned RPA-based data processing method.

[0145] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0146] The RPA-based data processing method, device, electronic equipment, and storage medium provided in this application embodiment involve: activating an RPA robot and calling a first interface to obtain a business event order; classifying and identifying the business event order using a pre-trained recommendation algorithm model; selecting a fixed solution from a fixed solution knowledge base based on the classification and identification results; feeding the fixed solution back to the corresponding processing object and obtaining the modification parameters uploaded by the processing object; effectively reducing user operation time and improving data modification processing efficiency by identifying business events through the RPA robot; assembling and filling the fixed solution according to the modification parameters to obtain a data modification solution; modifying the data in a first database according to the data modification solution to obtain a data report; calling a second interface to process the data report for approval to obtain an approval result; and when the approval result is approved, modifying the data in a second database according to the data modification solution to obtain a production execution log. This method reduces errors in manual processing through process automation, thereby reducing labor costs and improving data processing efficiency.

[0147] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0148] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0149] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0150] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0151] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0152] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0153] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0154] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0155] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0156] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0157] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A data processing method based on RPA, characterized in that, The method comprises: starting an RPA robot, calling a first interface to obtain a business event form; classifying and identifying the business event form through a pre-trained recommendation algorithm model, and selecting a fixed scheme from a fixed scheme knowledge base according to a classification and identification result; feeding back the fixed scheme to a corresponding processing object and obtaining modification parameters uploaded by the processing object; performing assembly and filling processing on the fixed scheme according to the modification parameters to obtain a data modification scheme; performing data modification processing on a first database according to the data modification scheme to obtain a data signing form; calling a second interface to perform signing and approval processing on the data signing form to obtain an approval result; when the approval result is approval, performing data modification processing on a second database according to the data modification scheme to obtain a production execution log; the data modification processing on the first database according to the data modification scheme to obtain the data signing form comprises: determining the first database according to the data modification scheme; calling a third interface to perform entry inspection processing on the first database to obtain an inspection result; calling the first interface to perform data modification processing on the first database according to the inspection result to obtain a first data modification result; calling the third interface to perform data verification processing on the first data modification result to obtain the data signing form.

2. The method of claim 1, wherein, the calling of the first interface to perform data modification processing on the first database according to the inspection result to obtain the first data modification result comprises: determining a data modification script according to the data modification scheme; calling the first interface to execute the data modification script to perform data modification processing on the first database according to the inspection result, and obtaining abnormal content of the data modification script; when the abnormal content of the data modification script is empty, obtaining the first data modification result; or, when the abnormal content of the data modification script is not empty, performing abnormal analysis on the abnormal content, feeding back an abnormal analysis result to a corresponding processing object, and obtaining a modification scheme uploaded by the processing object; updating the data modification script according to the modification scheme, and returning to the step of calling the first interface to execute the data modification script on the first database according to the inspection result, and obtaining the abnormal content of the data modification script.

3. The method of claim 1, wherein, the calling of the third interface to perform data verification processing on the first data modification result to obtain the data signing form comprises: determining a data verification script according to the data modification scheme; calling the third interface to execute the data verification script to perform data verification processing on the first data modification result, and obtaining a data verification result; when the data verification result is passed, calling the first interface to obtain the data signing form; or, when the data verification result is not passed, stopping the business event form and feeding back the data verification result to a corresponding processing object.

4. The method of claim 1, wherein, the calling of the second interface to perform signing and approval processing on the data signing form to obtain an approval result comprises: obtaining a signing and approval chain; The second interface is called to initiate a reporting and approval application for the data reporting form, the data reporting form is processed for approval according to the reporting and approval chain, and an approval state of the data reporting form is obtained; If the approval state of the data reporting form is approved, an approval result is obtained; Or, if the approval state of the data reporting form is not approved, the step of obtaining the approval state of the data reporting form is returned after waiting for a preset time length.

5. The method of claim 1, wherein, When the approval result is approved, the second database is processed for data modification according to the data modification scheme, and a production execution log is obtained, including: When the approval result is approved, the second database is determined according to the data modification scheme; The first interface is called to process the second database for data modification, and a second data modification result is obtained; The third interface is called to process the second data modification result for data verification, and the fourth interface is called after verification, and a production execution log is obtained.

6. The method of claim 1, wherein, The method further includes: The data modification scheme and the production execution log are obtained; The first interface is called to process the data modification scheme and the production execution log for system uploading, and an uploading result is notified to a corresponding processing object.

7. A data processing apparatus based on RPA, characterized in that, The device includes: The first module is configured to start an RPA robot, and call the first interface to obtain a business event form; The second module is configured to classify and identify the business event form through a pre-trained recommendation algorithm model, and select a fixed scheme from a fixed scheme knowledge base according to a classification and identification result; The third module is configured to feed back the fixed scheme to a corresponding processing object, and obtain modification parameters uploaded by the processing object; The fourth module is configured to assemble and fill the fixed scheme according to the modification parameters, and obtain a data modification scheme; The fifth module is configured to process a first database for data modification according to the data modification scheme, and obtain a data reporting form; The sixth module is configured to call the second interface to process the data reporting form for reporting and approval, and obtain an approval result; The seventh module is configured to process a second database for data modification according to the data modification scheme when the approval result is approved, and obtain a production execution log; The fifth module is configured to process the first database for data modification according to the data modification scheme, and obtain the data reporting form, including: The first database is determined according to the data modification scheme; The third interface is called to process the first database for entry inspection, and an inspection result is obtained; The first interface is called to process the first database for data modification according to the inspection result, and a first data modification result is obtained; The third interface is called to process the first data modification result for data verification, and the data reporting form is obtained.

8. An electronic device, comprising: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the data processing method of any one of claims 1 to 6 when executing the computer program.

9. A computer-readable storage medium storing a computer program, the computer-readable storage medium comprising: The computer program is executed by the processor to implement the data processing method of any one of claims 1 to 6.

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