ETF coupon filling method and device based on RPA and AI, equipment and medium

Abstract

The invention discloses an ETF coupon filling method and device based on RPA and AI, equipment and a medium, and the method comprises the steps: generating a visual interface comprising an input control, an output control and a task release control, responding to the triggering operation of the task release control of the visual interface, and sending a to-be-filled coupon source file to an RPA system; the RPA system logs in a target fund website corresponding to the target website based on the user login information, and downloads a first target coupon filling file from the target fund website based on the coupon filling demand information; the AI module performs data verification and data format conversion on the first target coupon filling file to obtain a second target coupon filling file after verification is passed; and the RPA system sends the second target coupon filling file to the output control. The ETF coupon filling task is completed through cooperation of the RPA system and the AI module, and compared with an existing manual coupon filling scheme, the ETF coupon filling efficiency and the accuracy of coupon filling data can be improved.

Description

Technical Field

[0001] The present application relates to but is not limited to the field of process automation technology, and in particular to an ETF stock replenishment method, device, equipment, and medium based on RPA and AI. Background Art

[0002] For cross-market ETF stock business, a large amount of ETF replenishment source data needs to be replenished in real time every day in a "time priority, price priority" manner to calculate the cost and fees of real-time replenishment for investors. In the ETF replenishment scenario, users first need to spend a lot of time to log in to the fund company website to download the ETF replenishment data. The ETF replenishment file obtained after downloading needs to be further compared and format converted. Due to the large number of fund company websites and the fact that it takes at least 3 minutes to download from each fund company website, it takes a lot of labor and time to complete the download, data comparison and format conversion of the ETF replenishment file, and the accuracy of the data comparison results cannot be guaranteed. Summary of the invention

[0003] The embodiments of the present application provide an ETF replenishment method, device, equipment, and medium based on RPA and AI, which can improve the efficiency of ETF replenishment and the accuracy of replenishment data.

[0004] In a first aspect, an embodiment of the present application provides an ETF stock replenishment method based on RPA and AI, including:

[0005] Generate a visualization interface, the visualization interface including an input control, an output control, and a task release control, wherein the input control is used to receive a source file of to-be-replenished securities, the source file of to-be-replenished securities including replenishment demand information, target ETF information, and user information, wherein the target ETF information includes a target ETF fund identifier and a target website corresponding to the target ETF fund identifier, and the user information includes user login data for the target website;

[0006] In response to the triggering operation of the task release control, the source file of the to-be-replaced coupons is sent to the RPA system;

[0007] The RPA system logs in to the target fund website corresponding to the target website address based on the user login information, and downloads the first target bond replenishment file from the target fund website based on the bond replenishment demand information;

[0008] The RPA system calls the AI ​​module to perform data verification and data format conversion on the first target supplementary bond file to obtain a corresponding second target supplementary bond file, wherein the data verification result corresponding to the second target supplementary bond file indicates that the verification has passed;

[0009] The RPA system sends the second target supplementary voucher file to the output control.

[0010] In some embodiments, the RPA system includes a plurality of RPA robots, the number of the target websites is multiple, the number of the user login information is multiple, each of the user login information uniquely corresponds to one target website, the RPA system logs in to the target fund website corresponding to the target website based on the user login information, and downloads the first target coupon replenishment file from the target fund website based on the coupon replenishment demand information, including:

[0011] Determine the status information of each of the RPA robots respectively, wherein each of the status information is used to indicate the task execution status and performance index of the corresponding RPA robot;

[0012] Based on all the status information, send a target number of target URLs and corresponding user login information to all the RPA robots;

[0013] Each of the RPA robots logs in to the target fund website corresponding to the target website based on the received user login information, and downloads the corresponding first target replenishment file from the target fund website based on the replenishment demand information, wherein one first target replenishment file corresponds to one target website.

[0014] In some embodiments, after downloading the first target bond replenishment file from the target fund website based on the bond replenishment demand information, the method further includes:

[0015] Sending the first target supplementary coupon file to the output control;

[0016] The sending the first target supplementary voucher file to the output control includes:

[0017] Each of the RPA robots sends the first target supplementary voucher file to the output control after downloading the first target supplementary voucher file.

[0018] or,

[0019] Each time the RPA robot completes downloading of a first target supplementary coupon file, it saves the first target supplementary coupon file to the cache space, monitors the reference number of the first target supplementary coupon files stored in the cache space, and when the reference number is greater than or equal to a preset number threshold, sends all the first target supplementary coupon files currently stored in the file queue to the output control, or monitors the download timestamp of each first target supplementary coupon file in the cache space, and sends the first target supplementary coupon file corresponding to the time length between the download timestamp and the current timestamp in the cache space that is greater than or equal to the preset time length to the output control.

[0020] In some embodiments, the RPA system calls the AI ​​module to perform data verification and data format conversion on the first target supplementary voucher file to obtain the corresponding second target supplementary voucher file, including:

[0021] Parsing the first target supplementary note file to obtain a plurality of first supplementary note fields, wherein the data types corresponding to the first supplementary note fields are different from each other;

[0022] Performing data field comparison on all the first supplementary coupon fields based on a preset data comparison rule to obtain a comparison result;

[0023] When the comparison result indicates that the comparison is successful, format conversion is performed on all the first supplementary note fields to obtain corresponding second supplementary note fields, wherein the data format corresponding to the second supplementary note fields is a readable format of the valuation system;

[0024] The second target supplementary note file is generated based on all the second supplementary note fields.

[0025] In some embodiments, data field comparison is performed on all the first supplementary ticket fields based on a preset data comparison rule to obtain a comparison result, including:

[0026] Acquire a verification file from the target website based on the user information, the verification file including second field formats of different data types in the target website and corresponding second field contents;

[0027] A data field comparison is performed based on the verification file and the first supplementary bill field. When the first field format of all the first supplementary bill fields is the same as the corresponding target field format, and the first field content of all the first supplementary bill fields is the same as the corresponding target field content, the comparison result indicates that the comparison is successful, wherein the target field format is the second field format of the same data type as that corresponding to the first field format, and the target field content is the second field content of the same data type as that corresponding to the first field format.

[0028] In some embodiments, the method further comprises:

[0029] When any of the first field formats is different from the corresponding target field format, a first warning message is generated based on the first ticket replenishment field and the corresponding target field format, and the first warning message is sent to the server corresponding to the visualization interface, so that the server feeds back the first warning message to the target user corresponding to the user information;

[0030] When the content of any of the first fields is different from the corresponding target field content, a second alarm message is generated based on the first field content and the corresponding target field content, and the second alarm message is sent to the server corresponding to the visualization interface, so that the server feeds back the second alarm message to the target user corresponding to the user information.

[0031] In some embodiments, sending the first target supplementary voucher file to the output control includes:

[0032] The RPA system calls the AI ​​module to delete the expired bond replenishment data, the bond replenishment direction abnormal data, and the ETF bond replenishment code abnormal data in the first target bond replenishment file to obtain the first target bond replenishment file after data preprocessing;

[0033] The first target supplementary coupon file after the data preprocessing is sent to the output control.

[0034] In a second aspect, an embodiment of the present application provides a control device, comprising at least one control processor and a memory for communicating with the at least one control processor; the memory stores instructions executable by the at least one control processor, and the instructions are executed by the at least one control processor so that the at least one control processor can execute the ETF stock replenishment method based on RPA and AI as described in the first aspect.

[0035] In a third aspect, an embodiment of the present application further provides an electronic device, comprising the control device of the second aspect.

[0036] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are used to execute the ETF stock replenishment method based on RPA and AI as described in the first aspect.

[0037] The embodiment of the present application provides an ETF replenishment method, device, equipment, and medium based on RPA and AI, the method comprising: generating a visualization interface, the visualization interface comprising an input control, an output control, and a task release control, wherein the input control is used to receive a source file of securities to be replenished, the source file of securities to be replenished comprising replenishment demand information, target ETF information, and user information, wherein the target ETF information comprises a target ETF fund identifier and a target website corresponding to the target ETF fund identifier, and the user information comprises user login data for the target website; responding to a trigger operation of the task release control, and sending the source file of securities to be replenished to an RPA system; the RPA system logs in to a target fund website corresponding to the target website based on the user login information, and downloads a first target replenishment file from the target fund website based on the replenishment demand information; the RPA system calls an AI module to perform data verification and data format conversion on the first target replenishment file to obtain a corresponding second target replenishment file, wherein the data verification result corresponding to the second target replenishment file indicates that the verification has passed; the RPA system sends the second target replenishment file to the output control. According to the solution provided in the embodiment of the present application, the ETF replenishment task is completed by cooperating with the RPA system and the AI ​​module. Compared with the existing manual replenishment solution, the ETF replenishment efficiency and the accuracy of the replenishment data can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 This is a flowchart of the steps of an ETF stock replenishment method based on RPA and AI provided by an embodiment of the present application;

[0039] Figure 2 It is a structural diagram of a control device provided in another embodiment of the present application. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0041] It is understood that although the functional modules are divided in the device schematic diagram and the logical order is shown in the flow chart, 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 flow chart. The terms "first", "second", etc. in the specification, claims or the above drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0042] For cross-market ETF stock business, a large amount of ETF replenishment source data needs to be replenished in real time every day in a "time priority, price priority" manner to calculate the cost and fees of real-time replenishment for investors. In the ETF replenishment scenario, users first need to spend a lot of time to log in to the fund company website to download the ETF replenishment data. The ETF replenishment file obtained after downloading needs to be further compared and format converted. Due to the large number of fund company websites and the fact that it takes at least 3 minutes to download from each fund company website, it takes a lot of labor and time to complete the download, data comparison and format conversion of the ETF replenishment file, and the accuracy of the data comparison results cannot be guaranteed.

[0043] To solve the above-mentioned problems, an embodiment of the present application provides an ETF replenishment method, device, equipment, and medium based on RPA and AI, the method comprising: generating a visual interface, the visual interface comprising an input control, an output control, and a task release control, wherein the input control is used to receive a source file of securities to be replenished, the source file of securities to be replenished comprises replenishment demand information, target ETF information, and user information, wherein the target ETF information comprises a target ETF fund identifier and a target website corresponding to the target ETF fund identifier, and the user information comprises user login data for the target website; in response to a trigger operation of the task release control, the source file of securities to be replenished is sent to the RPA system; the RPA system logs in to a target fund website corresponding to the target website based on the user login information, and downloads a first target replenishment file from the target fund website based on the replenishment demand information; the RPA system calls an AI module to perform data verification and data format conversion on the first target replenishment file to obtain a corresponding second target replenishment file, wherein the data verification result corresponding to the second target replenishment file indicates that the verification has passed; the RPA system sends the second target replenishment file to the output control. According to the solution provided in the embodiment of the present application, the ETF replenishment task is completed by cooperating with the RPA system and the AI ​​module. Compared with the existing manual replenishment solution, the ETF replenishment efficiency and the accuracy of the replenishment data can be improved.

[0044] The embodiments of the present application are further described below in conjunction with the accompanying drawings.

[0045] refer to Figure 1 , Figure 1 The following is a flowchart of the steps of the ETF replenishment method based on RPA and AI. The embodiment of the present application provides an ETF replenishment method based on RPA and AI, which includes but is not limited to the following steps:

[0046] Step S10, generating a visualization interface, the visualization interface includes input controls, output controls and task release controls, wherein the input controls are used to receive the source file of the to-be-replenished securities, the source file of the to-be-replenished securities includes replenishment demand information, target ETF information and user information, wherein the target ETF information includes the target ETF fund identifier and the target website corresponding to the target ETF fund identifier, and the user information includes user login data for the target website.

[0047] Specifically, the visualization interface of this embodiment is built with low code, and the input controls, output controls and task release controls in the visualization interface are composed of low-code basic controls after modifying the style through CSS. The visualization interface and the background exchange data through a pre-packaged interface, and the platform internal data corresponding to the visualization interface is saved to the database of the background RPA system.

[0048] Specifically, the visualization interface of this embodiment corresponds to a web page, which also includes a home page dashboard, a process supervision page, a fund company management page, a user configuration page and a system configuration page. The home page dashboard is used to display the performance indicator data of multiple time dimensions such as today, this week, this month, and this year, as well as the web page usage guidance information; the process supervision page is used to display the status of the current replenishment task (waiting for execution, executing, successful execution, failed execution) and the progress percentage. The process supervision page also provides a button for triggering operations such as termination, suspension, redoing, and downloading result files to the local computer for the ETF replenishment task. After the button is clicked, the current page is opened through the interface Send relevant commands to the background RPA system to enable the RPA system to perform relevant operations; the fund company management page is used to display multiple private equity fund products that users cooperate with, and provide configuration components for users to configure data conversion rules and account information for each private equity fund product in a unified manner; the user configuration page is used to provide user-defined configuration functions. The configuration data obtained on the user configuration page will be encrypted and saved in the database. When the background RPA system is executed, it will be read from the database and operated according to the user-configured data; the system configuration page is used to set the permissions and scheduled tasks of different users on different pages of this application, such as scheduled cleaning of junk files and data.

[0049] It can be understood that the visual interface of this embodiment can simplify user operations and support unified management of user accounts that log in to the websites of various fund companies through the visual interface, providing support for subsequent triggering and executing ETF replenishment tasks.

[0050] Step S20, in response to the triggering operation of the task release control, the source file of the coupons to be replenished is sent to the RPA system.

[0051] Specifically, the task release control of this embodiment is used to trigger the replenishment task and send the replenishment task to the RPA system. The task release control also has a task configuration function, which is divided into two parts: scheduled tasks and manual tasks. This page can be used to evoke the background RPA system and the AI ​​module to automatically complete the corresponding replenishment task. The scheduled task needs to configure the reading position and the planned execution time period corresponding to the source file of the to-be-replenished securities, so that the RPA system and the AI ​​module can read the source file of the ETF to-be-replenished securities according to the preset requirements to execute the corresponding ETF replenishment task. As a supplement to the scheduled task, the manual task can allow users to actively upload the source file of the to-be-replenished securities to trigger the start of a new replenishment task. The two parts can be triggered by clicking on the two sub-controls belonging to the task release control, and the corresponding configuration parameters are entered in the two sub-controls respectively; it can also be that the task release control includes an input box, after the configuration information of the corresponding source file of the to-be-replenished securities is entered into the input box, click to select the control "Scheduled Task" or "Manual Task".

[0052] Step S30: The RPA system logs in to the target fund website corresponding to the target URL based on the user login information, and downloads the first target replenishment file from the target fund website based on the replenishment demand information.

[0053] It can be understood that, referring to the description of the above embodiment, after the RPA system receives the source file of the securities to be replenished, the RPA system logs in to the target fund website corresponding to the target URL based on the user login information, and downloads the first target replenishment file from the target fund website based on the replenishment demand information, thereby providing an effective data basis for the subsequent completion of the ETF replenishment task.

[0054] In addition, in some embodiments, the RPA system includes multiple RPA robots, the number of target URLs is multiple, the number of user login information is multiple, and each user login information uniquely corresponds to one target URL. Figure 1 Step S30 includes but is not limited to the following steps:

[0055] Step S31, respectively determining status information of each RPA robot, wherein each status information is used to indicate a task execution status and performance index of the corresponding RPA robot;

[0056] Step S32: Based on all the status information, send a target number of target URLs and corresponding user login information to all RPA robots;

[0057] In step S33, each RPA robot logs in to the target fund website corresponding to the target URL based on the received user login information, and downloads the corresponding first target replenishment file from the target fund website based on the replenishment demand information, wherein one first target replenishment file corresponds to one target URL.

[0058] It should be noted that before the RPA system logs in to the target fund website corresponding to the target URL, this embodiment also includes the following method: the RPA system calls the AI ​​module to remove expired data, remove incorrectly formatted data, and other operations on the source file of the coupons to be replenished.

[0059] It is understandable that when the RPA system receives multiple replenishment tasks, if only one RPA robot is used to execute them, the task completion efficiency is low. Based on this, this embodiment determines the status information (including task execution status and performance indicators) of each RPA robot in the RPA system to allocate the number of replenishment tasks and specific replenishment tasks to each RPA robot, so that each RPA robot in the RPA system processes replenishment tasks in parallel, effectively improving the execution efficiency of ETF replenishment tasks. After each RPA robot receives the target number of target websites and corresponding user login information, it logs in to the target fund website corresponding to the target website based on the user login information, and downloads the corresponding first target replenishment file from the target fund website based on the replenishment demand information.

[0060] In addition, in some embodiments, when executing Figure 1 After step S30, the ETF stock replenishment method based on RPA and AI in this embodiment further includes but is not limited to the following steps:

[0061] Step S34, sending the first target supplementary coupon file to the output control;

[0062] Step S34 includes but is not limited to the following steps:

[0063] Step S341: after each RPA robot downloads a first target supplementary voucher file, it sends the first target supplementary voucher file to the output control;

[0064] or,

[0065] Step S342: After the RPA robot downloads each first target supplementary coupon file, it saves the first target supplementary coupon file to the cache space, monitors the reference number of first target supplementary coupon files stored in the cache space, and when the reference number is greater than or equal to the preset number threshold, sends all first target supplementary coupon files currently stored in the file queue to the output control, or monitors the download timestamps of each first target supplementary coupon file in the cache space, and sends the first target supplementary coupon file corresponding to the time length between the download timestamp and the current timestamp in the cache space that is greater than or equal to the preset time length to the output control.

[0066] It is understandable that the first target coupon file is the original coupon result file without format conversion. Figure 1Step S30, that is, after the RPA system completes downloading the first target supplementary voucher file, the method of this embodiment further includes: sending the first target supplementary voucher file to the output control of the visualization interface so that the corresponding user can download it on the visualization interface.

[0067] Specifically, in the embodiment of the present application, the first target supplementary voucher file is sent to the output control in the following two ways:

[0068] (1) Each RPA robot downloads a first target supplementary voucher file and sends the first target supplementary voucher file to the output control;

[0069] (2) After each RPA robot downloads a first target supplementary voucher file, it saves the first target supplementary voucher file to the cache space, monitors the reference number of first target supplementary voucher files stored in the cache space, and when the reference number is greater than or equal to a preset number threshold, sends all first target supplementary voucher files currently stored in the file queue to the output control, or monitors the download timestamps of each first target supplementary voucher file in the cache space, and sends the first target supplementary voucher file corresponding to the time length between the download timestamp and the current timestamp in the cache space that is greater than or equal to the preset time length to the output control.

[0070] It can be understood that the first method meets the real-time requirements of loading the replenishment result information to the user end; the second method is to temporarily store each first target replenishment file downloaded by the RPA robot in the cache space (the cache space in this embodiment is an Excel file), and limit the storage quantity and storage time of the cache space, thereby reducing the system overhead of the RPA system while meeting certain real-time requirements.

[0071] In addition, in some embodiments, step S34 also includes but is not limited to the following steps:

[0072] The RPA system calls the AI ​​module to delete the expired bond replenishment data, the abnormal bond replenishment direction data, and the abnormal ETF bond replenishment code data in the first target bond replenishment file, and obtains the first target bond replenishment file after data preprocessing;

[0073] The first target coupon file after data preprocessing is sent to the output control.

[0074] It is understandable that since the first target replenishment is the key to completing the subsequent replenishment task, by deleting the expired replenishment data, abnormal replenishment direction data and abnormal ETF replenishment code data in the first target replenishment file, it is possible to improve data accuracy, reduce the risk of subsequent operations and reduce system storage pressure.

[0075] It should be noted that the AI ​​module of this embodiment is an intelligent file analysis robot based on the RPA robot, which can parse and verify the file content. It has a built-in vectorized text model and review model, which measures the text format and the correlation between texts according to the text category, and can combine the configured parameters with the intelligent conjecture and deduction of the user's current scenario to realize the analysis and classification of text content and filter out information with unqualified content.

[0076] Step S40: The RPA system calls the AI ​​module to perform data verification and data format conversion on the first target replenishment document file to obtain a corresponding second target replenishment document file, wherein the data verification result corresponding to the second target replenishment document file indicates that the verification has passed;

[0077] It can be understood that the second target replenishment ticket file of this embodiment is obtained by automatically performing data verification and data format conversion on the first target replenishment ticket file through the AI ​​module. Compared with the existing manual data verification and manual data format conversion solutions, it can effectively improve the corresponding efficiency and accuracy.

[0078] In a specific embodiment, Figure 1 Step S40 includes but is not limited to the following steps:

[0079] Step S41, parsing the first target supplementary note file to obtain a plurality of first supplementary note fields, wherein the data types corresponding to the first supplementary note fields are different from each other;

[0080] Step S42, performing data field comparison on all the first supplementary ticket fields based on a preset data comparison rule to obtain a comparison result;

[0081] Step S43, when the comparison result indicates that the comparison is successful, format conversion is performed on all the first supplementary note fields to obtain corresponding second supplementary note fields, wherein the data format corresponding to the second supplementary note fields is a readable format of the valuation system;

[0082] Step S44: Generate a second target supplementary note file based on all the second supplementary note fields.

[0083] Specifically, the present embodiment parses the first target supplementary securities file through the AI ​​module to obtain multiple first supplementary securities fields, wherein the data types corresponding to the various first supplementary securities fields are different from each other, for example, the first supplementary securities fields include amount-type digital string formats, date string formats, etc.; based on the preset data comparison rules, data field comparison is performed on all the first supplementary securities fields to obtain a comparison result. The data comparison rules of the present embodiment are obtained from the corresponding target website, and different target websites correspond to different ETF fund codes; when the comparison result indicates that the comparison is successful, format conversion is performed on all the first supplementary securities fields to obtain corresponding second supplementary securities fields, wherein the data format corresponding to the second supplementary securities fields is a readable format of the valuation system, and a second target supplementary securities file is generated based on all the second supplementary securities fields, thereby ensuring the accuracy of the second target supplementary securities file.

[0084] In addition, in some embodiments, the ETF replenishment method based on RPA and AI also includes but is not limited to the following steps:

[0085] When any of the first field formats is different from the corresponding target field format, first warning information is generated based on the first ticket replenishment field and the corresponding target field format, and the first warning information is sent to the server corresponding to the visualization interface, so that the server feeds back the first warning information to the target user corresponding to the user information;

[0086] When the content of any first field is different from the corresponding target field content, a second alarm information is generated based on the first field content and the corresponding target field content, and the second alarm information is sent to the server corresponding to the visualization interface, so that the server feeds back the second alarm information to the target user corresponding to the user information.

[0087] It should be noted that, with reference to the description of the above embodiment, this embodiment determines whether to generate a second target replenishment ticket file based on the comparison result. When the format of any one of the first fields is different from the format of the corresponding target field, a first alarm message is generated based on the first replenishment ticket field and the corresponding target field format, and the first alarm message is sent to the server corresponding to the visualization interface, so that the server feeds back the first alarm message to the target user corresponding to the user information, and the second target replenishment ticket file is not generated, and the corresponding replenishment ticket task fails; or, when the content of any one of the first fields is different from the content of the corresponding target field, a second alarm message is generated based on the content of the first field and the corresponding target field content, and the second alarm message is sent to the server corresponding to the visualization interface, so that the server feeds back the second alarm message to the target user corresponding to the user information, and the second target replenishment ticket file is not generated, and the corresponding replenishment ticket task fails.

[0088] Specifically, the manner in which the first alarm information or the second alarm information is fed back to the corresponding target user in this embodiment depends on the specific type of contact information in the user information corresponding to the target user. When the contact information in the user information corresponding to the target user is an email number, the corresponding first alarm information or the second alarm information is fed back through the email number; when the contact information in the user information corresponding to the target user is a telephone number, the first alarm information or the second alarm information is fed back to the corresponding telephone number in the form of a text message; when the contact information in the user information corresponding to the target user corresponds to an email number and a telephone number, the alarm information is fed back in the above two ways.

[0089] Specifically, in some embodiments, step S42 includes but is not limited to the following steps:

[0090] Obtaining a verification file from the target website based on the user information, the verification file including second field formats of different data types in the target website and corresponding second field contents;

[0091] A data field comparison is performed based on the verification file and the first supplementary bill field. When the first field format of all the first supplementary bill fields is the same as the corresponding target field format, and the first field content of all the first supplementary bill fields is the same as the corresponding target field content, the comparison result indicates that the comparison is successful, wherein the target field format is a second field format of the same data type as that corresponding to the first field format, and the target field content is the second field content of the same data type as that corresponding to the first field format.

[0092] Specifically, the comparison items and comparison rules for the first supplementary ticket field and the verification file in this embodiment are shown in the verification logic list of Table 1:

[0093] Table 1 Verification logic list

[0094]

[0095]

[0096] In addition, in this embodiment, when the comparison result indicates that the comparison is successful, the step of performing format conversion on all the first supplementary bill fields to obtain the corresponding second supplementary bill fields specifically includes: performing data cleaning on all the first supplementary bill fields, and performing format conversion on the first supplementary bill fields after data cleaning to obtain the second supplementary bill fields.

[0097] Specifically, the rules for data cleaning and format conversion of the first supplementary ticket field in this embodiment are shown in the data cleaning and format conversion logic list in Table 2:

[0098] Table 2 Data cleaning and file conversion logic list

[0099]

[0100] Step S50: The RPA system sends the second target coupon replacement file to the output control.

[0101] It is understandable that the RPA system sends the second target security replenishment file to the output control so that the user can download the final ETF security replenishment result file by clicking the download control in the output control after logging into the visualization interface.

[0102] Specifically, referring to the description of the above embodiment, the specific method of sending the second target supplementary ticket file to the output control in this embodiment can refer to the method of sending the first target supplementary ticket file to the output control, and each time a second target supplementary ticket file is converted and generated, it can be sent to the output control; or it can be first stored in the cache space, and based on the cache timestamp and cache amount, multiple second target supplementary ticket files can be periodically sent to the output control.

[0103] like Figure 2 As shown, Figure 2 2 is a structural diagram of a control device provided in an embodiment of the present application. The present invention also provides a control device 200, including:

[0104] The processor 210 may be implemented by a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0105] The memory 220 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 220 can store an operating system and other applications. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 220, and the processor 210 calls and executes the ETF replenishment method based on RPA and AI in the embodiment of this application;

[0106] Input / output interface 230, used to implement information input and output;

[0107] Communication interface 240, used to realize communication interaction between the apparatus and other devices, which can be realized by wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.);

[0108] bus 250 , which transmits information between the various components of the device (e.g., processor 210 , memory 220 , input / output interface 230 , and communication interface 240 );

[0109] The processor 210 , the memory 220 , the input / output interface 230 , and the communication interface 240 are connected to each other in communication within the device via the bus 250 .

[0110] In addition, an embodiment of the present application further provides an electronic device, including the control device 200 of the above embodiment.

[0111] In addition, an embodiment of the present application also provides a storage medium, which is a computer-readable storage medium. The storage medium stores a computer program, and when the computer program is executed by a processor, it implements the above-mentioned ETF replenishment method based on RPA and AI.

[0112] As a non-transient computer-readable storage medium, the memory can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage devices. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and are implemented to be located in one place, or may also be distributed to multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment.

[0113] It will be appreciated by those skilled in the art that all or some of the steps and systems in the disclosed method above may be implemented as software, firmware, hardware and appropriate combinations thereof. Some physical components or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor or a microprocessor, or may be implemented as hardware, or may be implemented as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or a non-transitory medium) and a communication medium (or a temporary medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage or other magnetic storage devices, or any other medium that may be used to store desired information and may be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically include computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0114] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the above-mentioned implementation mode. Technical personnel familiar with the field can also make various equivalent deformations or substitutions under the shared conditions without violating the spirit of the present invention. These equivalent deformations or substitutions are all included in the scope defined by the claims of the present invention.

Claims

1. An ETF stock replenishment method based on RPA and AI, characterized in that: include: Generate a visualization interface, the visualization interface including an input control, an output control, and a task release control, wherein the input control is used to receive a source file of to-be-replenished securities, the source file of to-be-replenished securities including replenishment demand information, target ETF information, and user information, wherein the target ETF information includes a target ETF fund identifier and a target website corresponding to the target ETF fund identifier, and the user information includes user login data for the target website; In response to the triggering operation of the task release control, the source file of the to-be-replaced coupons is sent to the RPA system; The RPA system logs in to the target fund website corresponding to the target website address based on the user login information, and downloads the first target bond replenishment file from the target fund website based on the bond replenishment demand information; The RPA system calls the AI ​​module to perform data verification and data format conversion on the first target supplementary bond file to obtain a corresponding second target supplementary bond file, wherein the data verification result corresponding to the second target supplementary bond file indicates that the verification has passed; The RPA system sends the second target supplementary voucher file to the output control.

2. The ETF stock replenishment method based on RPA and AI according to claim 1 is characterized in that: The RPA system includes a plurality of RPA robots, the number of the target websites is a plurality, the number of the user login information is a plurality, each of the user login information uniquely corresponds to one target website, the RPA system logs in to a target fund website corresponding to the target website based on the user login information, and downloads a first target coupon replenishment file from the target fund website based on the coupon replenishment demand information, including: Determine the status information of each of the RPA robots respectively, wherein each of the status information is used to indicate the task execution status and performance index of the corresponding RPA robot; Based on all the status information, send a target number of target URLs and corresponding user login information to all the RPA robots; Each of the RPA robots logs in to the target fund website corresponding to the target website based on the received user login information, and downloads the corresponding first target replenishment file from the target fund website based on the replenishment demand information, wherein one first target replenishment file corresponds to one target website.

3. The ETF stock replenishment method based on RPA and AI according to claim 2 is characterized in that: After downloading the first target bond replenishment file from the target fund website based on the bond replenishment demand information, the method further includes: Sending the first target supplementary coupon file to the output control; The sending the first target supplementary voucher file to the output control includes: Each of the RPA robots sends the first target supplementary voucher file to the output control after downloading the first target supplementary voucher file. or, Each time the RPA robot completes downloading of a first target supplementary coupon file, it saves the first target supplementary coupon file to the cache space, monitors the reference number of the first target supplementary coupon files stored in the cache space, and when the reference number is greater than or equal to a preset number threshold, sends all the first target supplementary coupon files currently stored in the file queue to the output control, or monitors the download timestamp of each first target supplementary coupon file in the cache space, and sends the first target supplementary coupon file corresponding to the time length between the download timestamp and the current timestamp in the cache space that is greater than or equal to the preset time length to the output control.

4. The ETF stock replenishment method based on RPA and AI according to claim 1 is characterized in that: The RPA system calls the AI ​​module to perform data verification and data format conversion on the first target supplementary voucher file to obtain a corresponding second target supplementary voucher file, including: Parsing the first target supplementary note file to obtain a plurality of first supplementary note fields, wherein the data types corresponding to the first supplementary note fields are different from each other; Performing data field comparison on all the first supplementary coupon fields based on a preset data comparison rule to obtain a comparison result; When the comparison result indicates that the comparison is successful, format conversion is performed on all the first supplementary note fields to obtain corresponding second supplementary note fields, wherein the data format corresponding to the second supplementary note fields is a readable format of the valuation system; The second target supplementary note file is generated based on all the second supplementary note fields.

5. The ETF stock replenishment method based on RPA and AI according to claim 4 is characterized in that: Performing data field comparison on all the first supplementary coupon fields based on a preset data comparison rule to obtain a comparison result, including: Acquire a verification file from the target website based on the user information, the verification file including second field formats of different data types in the target website and corresponding second field contents; A data field comparison is performed based on the verification file and the first supplementary bill field. When the first field format of all the first supplementary bill fields is the same as the corresponding target field format, and the first field content of all the first supplementary bill fields is the same as the corresponding target field content, the comparison result indicates that the comparison is successful, wherein the target field format is the second field format of the same data type as that corresponding to the first field format, and the target field content is the second field content of the same data type as that corresponding to the first field format.

6. The ETF stock replenishment method based on RPA and AI according to claim 5 is characterized in that: The method further comprises: When any of the first field formats is different from the corresponding target field format, a first warning message is generated based on the first ticket replenishment field and the corresponding target field format, and the first warning message is sent to the server corresponding to the visualization interface, so that the server feeds back the first warning message to the target user corresponding to the user information; When the content of any of the first fields is different from the corresponding target field content, a second alarm message is generated based on the first field content and the corresponding target field content, and the second alarm message is sent to the server corresponding to the visualization interface, so that the server feeds back the second alarm message to the target user corresponding to the user information.

7. The ETF stock replenishment method based on RPA and AI according to claim 3 is characterized in that: The sending the first target supplementary voucher file to the output control includes: The RPA system calls the AI ​​module to delete the expired bond replenishment data, the bond replenishment direction abnormal data, and the ETF bond replenishment code abnormal data in the first target bond replenishment file to obtain the first target bond replenishment file after data preprocessing; The first target supplementary coupon file after the data preprocessing is sent to the output control.

8. A control device, characterized in that: It includes at least one control processor and a memory for communicating with the at least one control processor; the memory stores instructions that can be executed by the at least one control processor, and the instructions are executed by the at least one control processor so that the at least one control processor can execute the ETF stock replenishment method based on RPA and AI as described in any one of claims 1 to 7.

9. An electronic device, characterized in that: Includes the control device as claimed in claim 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the ETF stock replenishment method based on RPA and AI as described in any one of claims 1 to 7.

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