Enterprise mobile banking user data processing method and RPA robot

Abstract

The embodiment of the present application provides a method for processing enterprise mobile banking user data and an RPA robot, which can be used in the field of artificial intelligence technology. The method includes: automatically obtaining the target enterprise mobile banking user data corresponding to each bank branch in the first time period from the report platform of the target bank based on the RPA extraction rules; according to the pre-acquired historical enterprise mobile banking user data corresponding to each bank branch in the second time period and the target enterprise mobile banking user data corresponding to the first time period, respectively determine the user activity change data of the enterprise mobile banking users corresponding to each bank branch, wherein the second time period is earlier than the first time period. The present application can improve the efficiency and automation of enterprise mobile banking user data processing; and further can effectively improve the timeliness and reliability of market operations and enterprise mobile banking product improvements based on changes in enterprise user activity.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, in particular to the field of artificial intelligence technology, and specifically to a method for processing enterprise mobile banking user data and an RPA robot. Background Art

[0002] With the rapid growth of mobile phone users, banks and other financial institutions have launched mobile banking. Among them, corporate mobile banking is a mobile financial service platform built for corporate users. Banks' corporate customers can quickly handle a variety of financial services through corporate mobile banking. In order to proactively obtain corporate users' feedback on corporate mobile banking for market operations and mobile banking product improvements, it is necessary to obtain corporate mobile banking user data in real time or regularly.

[0003] At present, the existing APP user data acquisition method can be a manual statistics of APP user data with poor automation and low efficiency. In order to improve the acquisition efficiency and automation, the existing APP user data acquisition method can also extract the target APP domain name database through the operator's DNS resolution server, and filter out the user statistics of the target APP from the different mobile APP data stored in the target APP domain name database. However, this method requires the participation of the operator, so the efficiency of APP user data acquisition is still low. In other words, the existing APP user data acquisition methods cannot meet the efficiency and automation requirements of corporate mobile banking user data processing. Summary of the invention

[0004] In response to the problems in the prior art, the present application provides a corporate mobile banking user data processing method and an RPA robot, which can improve the efficiency and automation of corporate mobile banking user data processing on the basis of effectively reducing the complexity of corporate mobile banking user data processing; can intuitively and proactively obtain corporate mobile banking users' user activity changes based on user activity change data, thereby effectively improving the timeliness and reliability of market operations and corporate mobile banking product improvements based on the corporate user activity changes.

[0005] In order to solve the above technical problems, this application provides the following technical solutions:

[0006] In a first aspect, the present application provides a method for processing enterprise mobile banking user data, comprising:

[0007] Automatically obtain the target enterprise mobile banking user data corresponding to each bank branch in the first time period from the target bank's reporting platform based on RPA extraction rules;

[0008] Based on the pre-acquired historical corporate mobile banking user data corresponding to each of the bank branches in the second time period and the target corporate mobile banking user data corresponding to the first time period, the user activity change data of the corporate mobile banking users corresponding to each of the bank branches are determined respectively, wherein the second time period is earlier than the first time period.

[0009] Furthermore, the method of automatically obtaining the target enterprise mobile banking user data corresponding to each bank branch in the first time period from the report platform of the target bank based on the RPA extraction rule includes:

[0010] Automatically log in to the reporting platform of the target bank according to the preset reporting platform login information;

[0011] Automatically searching the business data report module of the enterprise mobile banking in the report platform based on a preset report search rule, wherein the business data report module contains the correspondence between bank branches, enterprise mobile banking users and active user data;

[0012] According to the preset target RPA extraction rules, the target enterprise mobile banking user data corresponding to each of the bank branches within the first time period are extracted in the business data report module, wherein the target enterprise mobile banking user data includes active user data of at least one enterprise mobile banking user corresponding to a bank branch.

[0013] Furthermore, before automatically acquiring the target enterprise mobile banking user data corresponding to each bank branch in the first time period from the report platform of the target bank based on the RPA extraction rule, it also includes:

[0014] Respectively receive and store report platform login information, report search rules, and target RPA extraction rules for logging into the report platform of the target bank;

[0015] The report search rule includes the unique identifier of the enterprise mobile banking and the unique identifier of the business data report module;

[0016] The target RPA extraction rules include: extraction frequency, target extraction start time, first time period, unique identifiers corresponding to each bank branch, and extraction order of each bank branch.

[0017] Furthermore, before determining the user activity change data of the enterprise mobile banking users corresponding to each of the bank outlets respectively according to the pre-acquired historical enterprise mobile banking user data corresponding to each of the bank outlets in the second time period and the target enterprise mobile banking user data corresponding to each of the first time period, the method further includes:

[0018] Respectively receive and store the report platform login information, report search rules and historical RPA extraction rules for logging into the target bank's report platform; wherein the report search rules include the unique identifier of the enterprise mobile banking and the unique identifier of the business data report module; the historical RPA extraction rules include: extraction frequency, historical extraction start time, second time period, the unique identifier corresponding to each bank branch, and the extraction order of each bank branch;

[0019] Automatically log in to the target bank's reporting platform according to the reporting platform login information;

[0020] Automatically searching the business data report module of the enterprise mobile banking in the report platform based on the report search rule, wherein the business data report module contains the correspondence between bank branches, enterprise mobile banking users and active user data;

[0021] According to the historical RPA extraction rules, the historical corporate mobile banking user data corresponding to each of the bank branches in the second time period are extracted in the business data report module, wherein the historical corporate mobile banking user data includes active user data of at least one corporate mobile banking user corresponding to a bank branch.

[0022] Further, the user activity change data of the enterprise mobile banking users corresponding to each of the bank branches are determined respectively according to the pre-acquired historical enterprise mobile banking user data corresponding to each of the bank branches in the second time period and the target enterprise mobile banking user data corresponding to each of the bank branches in the first time period, including:

[0023] Importing the pre-acquired historical corporate mobile banking user data corresponding to each of the bank outlets in the second time period and the target corporate mobile banking user data corresponding to the first time period into the summary table corresponding to each of the bank outlets;

[0024] Automatically obtain the target user activity of the enterprise mobile banking users corresponding to each of the bank outlets in the first time period based on the summary table, and obtain the historical user activity of the enterprise mobile banking users corresponding to each of the bank outlets in the second time period;

[0025] The user activity change data of the corporate mobile banking users corresponding to each of the bank branches are determined respectively according to the target user activity and the historical user activity corresponding to each of the bank branches.

[0026] Furthermore, it also includes:

[0027] Sorting the bank branches in descending order according to the values ​​of the user activity change data to form a user activity ranking result;

[0028] Output the user activity ranking result.

[0029] Furthermore, the active user data includes: the total number of users whose frequency of logging into the corporate mobile banking via U-Shield or non-U-Shield exceeds a preset number of times within a preset period of time.

[0030] In a second aspect, the present application provides an RPA robot, including:

[0031] A data extraction module is used to automatically obtain the target enterprise mobile banking user data corresponding to each bank branch in the first time period from the target bank's report platform based on the RPA extraction rules;

[0032] A data processing module is used to determine the user activity change data of the corporate mobile banking users corresponding to each of the bank branches according to the pre-acquired historical corporate mobile banking user data corresponding to each of the bank branches in the second time period and the target corporate mobile banking user data corresponding to the first time period, wherein the second time period is earlier than the first time period.

[0033] In a third aspect, the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the corporate mobile banking user data processing method when executing the program.

[0034] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the corporate mobile banking user data processing method.

[0035] It can be seen from the above technical solution that the present application provides a method for processing enterprise mobile banking user data, including: automatically obtaining the target enterprise mobile banking user data corresponding to each bank branch in the first time period from the report platform of the target bank based on the RPA extraction rule; determining the user activity change data of the enterprise mobile banking users corresponding to each bank branch respectively according to the pre-acquired historical enterprise mobile banking user data corresponding to each of the bank branches in the second time period and the target enterprise mobile banking user data corresponding to the first time period, wherein the second time period is earlier than the first time period; by directly extracting the operation data of the enterprise mobile banking from the internal system of the target bank, and taking the bank branch as the acquisition unit, in the process of acquiring the enterprise mobile banking user data, there is no need to communicate with third-party objects such as suppliers in real time, thereby effectively reducing the complexity of the enterprise mobile banking user data processing, and effectively improving the efficiency of the enterprise mobile banking user data acquisition and reducing data redundancy; by adopting the RPA extraction rule and directly extracting the operation data of the enterprise mobile banking from the internal system of the target bank Means, combined with the method of taking bank branches as acquisition units, can apply RPA technology to further improve the automation level of enterprise mobile banking user data processing; by determining the user activity change data of the enterprise mobile banking users corresponding to each of the bank branches respectively according to the pre-acquired historical enterprise mobile banking user data corresponding to each of the bank branches in the second time period and the target enterprise mobile banking user data corresponding to each of the bank branches in the first time period, it is possible to intuitively and actively obtain the enterprise user activity change situation of the enterprise mobile banking users according to the user activity change data on the basis of obtaining the enterprise mobile banking user data, thereby effectively improving the timeliness and reliability of market operations and enterprise mobile banking product improvements based on the enterprise user activity change situation, effectively improving the user experience of the target bank, and at the same time, according to the enterprise user activity change situation, informing the connected bank branches to timely and specifically obtain the feedback from the connected enterprise users on the use of enterprise mobile banking, thereby further improving the efficiency of operations such as function optimization of enterprise mobile banking, so as to simultaneously improve the user experience of enterprise users using enterprise mobile banking. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0037] Figure 1It is a schematic diagram of the relationship between the RPA robot, the reporting platform of the target bank and the client device in the embodiment of the present application.

[0038] Figure 2 This is a first flow chart of the method for processing corporate mobile banking user data in an embodiment of the present application.

[0039] Figure 3 This is a second flow chart of the method for processing corporate mobile banking user data in an embodiment of the present application.

[0040] Figure 4 It is a third flow chart of the method for processing corporate mobile banking user data in the embodiment of the present application.

[0041] Figure 5 This is a fourth flow chart of the method for processing corporate mobile banking user data in an embodiment of the present application.

[0042] Figure 6 This is a fifth flow chart of the method for processing corporate mobile banking user data in the embodiment of the present application.

[0043] Figure 7 This is a sixth flow chart of the method for processing corporate mobile banking user data in the embodiment of the present application.

[0044] Figure 8 It is a schematic diagram of the structure of the RPA robot in the embodiment of the present application.

[0045] Fig. 9 It is a flowchart of extracting data provided in the application example of this application.

[0046] Fig.10 It is a flowchart of processing data provided in the application example of this application.

[0047] Fig.11 It is a schematic diagram of the structure of an electronic device in an embodiment of the present application. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0049] It should be noted that the enterprise mobile banking user data processing method and RPA robot disclosed in this application can be used in the fields of artificial intelligence technology and financial technology, and can also be used in any field other than the fields of artificial intelligence technology and financial technology. The application field of the enterprise mobile banking user data processing method and RPA robot disclosed in this application is not limited.

[0050] In one or more embodiments of the present application, the mobile banking refers to the designated APP of the target bank carried by the bank on a smart phone, which enables the bank user to use the banking services of the target bank in the mobile banking APP on this mobile terminal, and can use the mobile banking APP installed in the mobile terminal or other mobile devices to achieve the connection between the bank user and the target bank, and handle relevant banking business or provide financial services for the bank user. Among them, the corporate mobile banking is the mobile banking APP of the target bank specifically for its corporate customers (i.e. corporate users).

[0051] It is understandable that in financial institutions such as banks, each bank branch under the target bank's branches and other institutions is connected to different corporate users (or corporate mobile banking users). For example, bank branch A is connected to corporate user 1 and corporate user 2; bank branch B is connected to corporate user 3, corporate user 4 and corporate user 5.

[0052] In one or more embodiments of the present application, the active users in the enterprise mobile banking may also be referred to as: dynamic accounts, which refer to users who log in to the enterprise mobile banking more frequently than a preset number of times within a preset period of time. For example, if the frequency of enterprise user 3 logging in to the enterprise mobile banking exceeds 3 times (preset number of times) within 1 month (preset period of time), then enterprise user 3 may be considered a dynamic account, that is, an active user. Similarly, the user activity mentioned in one or more embodiments of the present application refers to the proportion of active users in the first time period, and the user activity may also be referred to as the dynamic account rate.

[0053] For example, in order to timely understand the number of mobile banking accounts of enterprises at all levels under its subordinate institutions, it is necessary to extract the average number of mobile banking accounts per month for assessment and promotion and mobile banking product upgrades. However, the process of manually extracting the average number of mobile banking accounts per month is repetitive and has fixed rules, large workload and calculation, prone to errors, and labor-intensive; even if the user statistics of mobile banking are screened by operators, there are still problems such as low efficiency caused by the need to rely on the participation of operators in real time.

[0054] Based on this, this application needs to provide a highly automated and efficient enterprise mobile banking user data processing method. Since the enterprise mobile banking data is stored in the target bank's internal systems such as the reporting platform, and different enterprise users are connected by different bank branches, therefore, considering the above-mentioned uniqueness of enterprise mobile banking, in order to effectively improve the efficiency and automation of enterprise mobile banking user data processing, this application directly extracts the operation data of enterprise mobile banking from the target bank's internal system, and uses bank branches as the acquisition unit. In the process of acquiring enterprise mobile banking user data, there is no need to communicate with suppliers and other third-party objects in real time. From the above content, it can be seen that based on the special characteristics of the target bank's control over enterprise mobile banking, the enterprise mobile banking user data processing method provided in the embodiment of this application is impossible to achieve using the existing APP user data acquisition method. At the same time, the enterprise mobile banking user data processing method provided in the embodiment of this application not only utilizes the special characteristics of the target bank's control over enterprise mobile banking, but also innovatively combines this special characteristic with RPA technology, etc., to form an efficient, accurate, highly automated and reliable enterprise mobile banking user data processing method. Specifically:

[0055] 1) This application directly extracts the operation data of corporate mobile banking from the internal system of the target bank, and uses bank branches as the acquisition unit. In the process of acquiring corporate mobile banking user data, there is no need to communicate with third-party objects such as suppliers in real time, thereby effectively reducing the complexity of corporate mobile banking user data processing, effectively improving the efficiency of corporate mobile banking user data acquisition and reducing data redundancy;

[0056] 2) This application uses RPA extraction rules and the method of directly extracting corporate mobile banking operation data from the target bank's internal system in 1), combined with the method of using bank branches as acquisition units, to apply RPA technology to further improve the degree of automation of corporate mobile banking user data processing.

[0057] 3) By determining the user activity change data of the corporate mobile banking users corresponding to each of the bank branches respectively based on the pre-acquired historical corporate mobile banking user data corresponding to each of the bank branches in the second time period and the target corporate mobile banking user data corresponding to each of the bank branches in the first time period, it is possible to intuitively and proactively obtain the corporate user activity change status of the corporate mobile banking users based on the user activity change data on the basis of obtaining the corporate mobile banking user data, thereby effectively improving the timeliness and reliability of market operations and corporate mobile banking product improvements based on the corporate user activity change status, effectively improving the user experience of the target bank, and at the same time, informing the connected bank branches based on the corporate user activity change status to obtain timely and targeted feedback on the use of corporate mobile banking by its connected corporate users, thereby further improving the efficiency of operations such as function optimization of corporate mobile banking, and simultaneously improving the user experience of corporate users using corporate mobile banking.

[0058] The RPA, robotic process automation or RPA (Robotic process automation) robot mentioned in one or more embodiments of the present application is an application that provides another way to automate the end user's manual operation process by imitating the end user's manual operation on the computer.

[0059] Based on the above content, the present application also provides an enterprise mobile banking user data processing device for implementing the enterprise mobile banking user data processing method provided in one or more embodiments of the present application, and the enterprise mobile banking user data processing device can be an RPA robot, see Figure 1, the RPA robot is communicated with the reporting platform of the target bank, and the reporting platform can also be communicated with the corporate mobile banking backend server to record the operating business data of the corporate mobile banking; the RPA robot can also communicate with the client device held by the tester by itself or through a third-party server, etc., to receive the corporate mobile banking user data processing request sent by the client device corresponding to each tester, so that the RPA robot logs in to the reporting platform of the target bank based on the corporate mobile banking user data processing request; then, based on the RPA extraction rules, the target corporate mobile banking user data corresponding to each bank branch in the first time period is automatically obtained from the reporting platform of the target bank; and according to the pre-acquired data of each bank branch in the second time period The RPA robot can use the historical corporate mobile banking user data corresponding to the respective time periods and the target corporate mobile banking user data corresponding to the respective time periods in the first time period to respectively determine the user activity change data of the corporate mobile banking users corresponding to each of the bank outlets, wherein the second time period is earlier than the first time period; thereafter, the RPA robot can send the user activity change data of the corporate mobile banking users corresponding to each of the bank outlets to the corresponding client device for display, so that the bank technicians can conduct market operations and corporate mobile banking product improvements in a timely and reliable manner based on the user activity change data of the corporate mobile banking users displayed on the client device, and can also conduct service assessments on each bank outlet based on the user activity change data of the corporate mobile banking users.

[0060] It is understandable that the client device may include a smart phone, a tablet electronic device, a network set-top box, a portable computer, a desktop computer, a personal digital assistant (PDA), a vehicle-mounted device, a smart wearable device, etc. Among them, the smart wearable device may include smart glasses, a smart watch, a smart bracelet, etc.

[0061] The client device may have a communication module (i.e., a communication unit) that can communicate with a remote server to achieve data transmission with the server. The server may include a server on the task scheduling center side, and other implementation scenarios may also include a server on an intermediate platform, such as a server on a third-party server platform that has a communication link with the task scheduling center server. The server may include a single computer device, or a server cluster consisting of multiple servers, or a server structure of a distributed device.

[0062] The server and the client device may communicate with each other using any suitable network protocol, including network protocols that have not yet been developed on the date of filing this application. The network protocols may include, for example, TCP / IP, UDP / IP, HTTP, HTTPS, etc. Of course, the network protocols may also include, for example, RPC (Remote Procedure Call Protocol) and REST (Representational State Transfer) protocols used on top of the above protocols.

[0063] The details are described in detail through the following embodiments and application examples.

[0064] In order to solve the problem that the existing APP user data acquisition method cannot meet the efficiency and automation requirements of enterprise mobile banking user data processing, this application provides an embodiment of an enterprise mobile banking user data processing method, see Figure 2 The enterprise mobile banking user data processing method executed by the RPA robot specifically includes the following contents:

[0065] Step 100: Automatically obtain the target enterprise mobile banking user data corresponding to each bank branch in the first time period from the target bank's reporting platform based on the RPA extraction rules.

[0066] In one or more embodiments of the present application, the enterprise mobile banking user data may be the registered user data of the enterprise mobile banking, and then it is necessary to obtain active user data whose activity meets preset requirements from these registered user data; of course, the enterprise mobile banking user data may also be active user data whose activity meets preset requirements, so as to further improve the efficiency and convenience of obtaining user activity change data of enterprise mobile banking users.

[0067] The reporting platform refers to one of the internal systems of the target bank, and specifically can be the target bank's group reporting platform CS+, etc. In a specific example, the target bank's reporting platform is used to store the user data of corporate users connected to each bank branch under the target bank in the corporate mobile banking.

[0068] Step 200: Determine the user activity change data of the corporate mobile banking users corresponding to each of the bank branches respectively based on the pre-acquired historical corporate mobile banking user data corresponding to each of the bank branches in the second time period and the target corporate mobile banking user data corresponding to each of the first time period, wherein the second time period is earlier than the first time period.

[0069] It is understandable that the target enterprise mobile banking user data refers to the user data of the enterprise users connected by each bank branch in the first time period in the target enterprise mobile banking. The historical enterprise mobile banking user data refers to the user data of the enterprise users connected by each bank branch in the second time period in the target enterprise mobile banking.

[0070] The corresponding relationship between the first time period and the second time period earlier than the first time period specifically includes:

[0071] (1) The first time period and the second time period are the same time period in different years and have the same length:

[0072] For example, the first time period is from February to April of this year; the second time period is from February to April of the previous year. Then, when determining the user activity change data of corporate user 1 corresponding to bank branch A, the target corporate mobile banking user data A1 from February to April of this year and the historical mobile banking user data A2 from February to April of the previous year can be obtained, that is:

[0073] (A1-A2) / A2×100%

[0074] (2) The first time period and the second time period belong to different years, and the second time period covers the first time period, but the time lengths are different:

[0075] For example, the first time period is from February to April of this year; the second time period is from January to May of the historical year. When determining the user activity change data of corporate user 1 corresponding to bank branch A, it is necessary to first determine the measurement unit of the user activity change data (for example, within 1 month, 1 week or 1 day), etc.; taking the acquisition of the monthly average user activity change data as an example, the monthly average target corporate mobile banking user data A11 of this year can be obtained based on the target corporate mobile banking user data A1 from February to April of this year; and the monthly average historical mobile banking user data A21 can be obtained based on the historical mobile banking user data A2 from January to May of the historical year, and then the monthly average user activity change data can be obtained based on the monthly average target corporate mobile banking user data A11 and the monthly average historical mobile banking user data A21, that is:

[0076] (A11-A21) / A21×100%, A11=A1 / 3, A21=A2 / 5;

[0077] (3) The first time period and the second time period belong to different years, and the time period of the second time period does not cover the first time period:

[0078] In this case, if the first time period and the second time period are different in length, the calculation method in (2) above can be used to obtain the monthly average user activity change data; if the first time period and the second time period are the same in length, the calculation method in (1) or (2) above can be used to obtain the monthly average user activity change data. Of course, this method obviously does not fall within the scope of obtaining the "year-on-year growth" user activity change situation. Therefore, the embodiment of the present application preferably adopts the calculation method in (1) or (2) above.

[0079] (4) The first and second time periods belong to the same year:

[0080] In this case, if the first time period and the second time period are different in length, the calculation method in (2) above can be used to obtain the monthly average user activity change data; if the first time period and the second time period are the same in length, the calculation method in (1) or (2) above can be used to obtain the monthly average user activity change data, and this method can be used to obtain the "month-on-month growth" user activity change situation. It can be understood that since the second time period is earlier than the first time period, this calculation method is only applicable to the situation where the first time period is not January of the current year.

[0081] In an example of an embodiment of the present application, two copies of historical corporate mobile banking user data can be collected in advance, one copy is the historical data of (1) or (2) mentioned above and belongs to a different year than the target corporate mobile banking user data, and the other copy is the historical data of (4) belonging to the same year as the target corporate mobile banking user data. Then, (4) and one of (1) or (2) mentioned above are executed in parallel, and the user activity change data obtained then includes the "month-on-month growth" and "year-on-year growth" data, which can further improve the intelligence and reliability of market operations and corporate mobile banking product improvements based on the changes in the corporate user activity.

[0082] From the above description, it can be seen that the enterprise mobile banking user data processing method provided in the embodiment of the present application directly extracts the enterprise mobile banking operation data from the internal system of the target bank, and takes the bank branch as the acquisition unit. In the process of acquiring the enterprise mobile banking user data, there is no need to communicate with third-party objects such as suppliers in real time, thereby effectively reducing the complexity of enterprise mobile banking user data processing, effectively improving the efficiency of enterprise mobile banking user data acquisition and reducing data redundancy; by adopting RPA extraction rules and the means of directly extracting the enterprise mobile banking operation data from the internal system of the target bank, combined with the method of taking the bank branch as the acquisition unit, RPA technology can be applied to further improve the automation of enterprise mobile banking user data processing; by pre-acquired historical enterprise mobile banking user data corresponding to each of the bank branches in the second time period According to the target corporate mobile banking user data corresponding to each of the above-mentioned bank branches and the target corporate mobile banking user data corresponding to the above-mentioned target corporate mobile banking user data in the above-mentioned first time period, the user activity change data of the corporate mobile banking users corresponding to each of the above-mentioned bank branches are determined respectively. On the basis of obtaining the corporate mobile banking user data, the corporate user activity change situation of the corporate mobile banking users can be intuitively and proactively obtained according to the user activity change data, thereby effectively improving the timeliness and reliability of market operations and corporate mobile banking product improvements based on the corporate user activity change situation, and effectively improving the user experience of the target bank. At the same time, according to the corporate user activity change situation, the connected bank branches can be informed of the corporate users’ feedback on the use of the corporate mobile banking in a timely and targeted manner, thereby further improving the efficiency of operations such as the function optimization of the corporate mobile banking, and simultaneously improving the user experience of corporate users using the corporate mobile banking.

[0083] In order to improve the efficiency, automation and reliability of the business data report module of the enterprise mobile banking that is automatically searched from the report platform, an embodiment of the enterprise mobile banking user data processing method provided in this application is provided. Figure 3 , step 100 in the enterprise mobile banking user data processing method specifically includes the following contents:

[0084] Step 110: Automatically log in to the report platform of the target bank according to the preset report platform login information;

[0085] It can be understood that the reporting platform login information may include information such as a user name and password for automatically logging into the reporting platform of the target bank, so that the RPA robot can automatically log in to the reporting platform of the target bank according to the reporting platform login information it receives.

[0086] Step 120: Automatically search the business data report module of the enterprise mobile banking in the report platform based on preset report search rules, wherein the business data report module contains the correspondence between bank branches, enterprise mobile banking users and active user data.

[0087] It can be understood that the business data report module can specifically be a daily (monthly, quarterly, annual) report module for the enterprise mobile banking certificate version business status. The business data report module contains multiple data reports, and the correspondence between bank branches, enterprise mobile banking users and active user data is stored in these data reports.

[0088] Step 130: According to the preset target RPA extraction rules, the target enterprise mobile banking user data corresponding to each of the bank branches within the first time period is extracted in the business data report module, wherein the target enterprise mobile banking user data includes active user data of at least one enterprise mobile banking user corresponding to a bank branch.

[0089] From the above description, it can be seen that the corporate mobile banking user data processing method provided in the embodiment of the present application can effectively improve the efficiency, automation and reliability of automatically searching for the business data reporting module of the corporate mobile banking in the reporting platform by automatically logging into the reporting platform of the target bank. By automatically searching for the business data reporting module of the corporate mobile banking in the reporting platform based on preset report search rules, the efficiency, automation and reliability of obtaining the target corporate mobile banking user data can be further improved.

[0090] In order to improve the automation and efficiency of logging into the report platform of the target bank, automatically searching the business data report module of the enterprise mobile banking in the report platform, and respectively extracting the target enterprise mobile banking user data corresponding to each bank branch in the first time period in the business data report module, an embodiment of the enterprise mobile banking user data processing method provided in the present application is provided, see Figure 4 The method for processing user data of enterprise mobile banking includes the following contents before step 100:

[0091] Step 010: Receive and store respectively the report platform login information, report search rules and target RPA extraction rules for logging into the target bank's report platform; wherein the report search rules include the unique identifier of the enterprise mobile banking and the unique identifier of the business data report module; the target RPA extraction rules include: extraction frequency, target extraction start time, first time period, the unique identifier corresponding to each bank branch and the extraction order of each bank branch.

[0092] Among them, with respect to the specific content in the report search rule, the automatic search for the business data report module of the corporate mobile banking in the report platform based on the preset report search rule in step 120 may specifically include the following content:

[0093] Step 121: searching for each report module corresponding to the corporate mobile banking in the report platform of the target bank based on the unique identifier of the corporate mobile banking;

[0094] Step 122: searching the business data report module of the enterprise mobile banking in each report module corresponding to the enterprise mobile banking according to the unique identifier of the business data report module.

[0095] In addition, with respect to the specific content in the target RPA extraction rule, the step 130 of extracting the target enterprise mobile banking user data corresponding to each bank branch in the first time period in the business data report module according to the preset target RPA extraction rule may specifically include the following content:

[0096] Step 131: Filtering the business data report within the first time period in the business data report module of the enterprise mobile banking according to the extraction frequency, the extraction start time and the first time period;

[0097] Step 132: selecting a bank branch as the target bank branch to be currently extracted according to the extraction order of the bank branches;

[0098] Step 133: determining the unique identifier of the target bank branch according to the unique identifiers corresponding to the respective bank branches, and then searching the business data report within the first time period for active user data of all corporate mobile banking users corresponding to the target bank branch according to the unique identifier of the target bank branch;

[0099] Step 134: Store the active user data of all corporate mobile banking users corresponding to the target bank branch within the first time period, then replace the current target bank branch according to the extraction order of the bank branches, and return to execute step 133 until the active user data of all corporate mobile banking users corresponding to all bank branches within the first time period have been acquired and stored.

[0100] From the above description, it can be seen that the corporate mobile banking user data processing method provided in the embodiment of the present application, by pre-setting the reporting platform login information, report search rules and target RPA extraction rules, can effectively improve the automation level and efficiency of logging into the target bank's reporting platform, automatically searching for the business data reporting module of the corporate mobile banking in the reporting platform, and extracting the target corporate mobile banking user data corresponding to each of the bank branches in the first time period in the business data reporting module.

[0101] In order to improve the automation and efficiency of logging into the report platform of the target bank, automatically searching the business data report module of the enterprise mobile banking in the report platform, and respectively extracting the historical enterprise mobile banking user data corresponding to each bank branch in the second time period in the business data report module, an embodiment of the enterprise mobile banking user data processing method provided in the present application is provided, see Figure 5 The method for processing user data of enterprise mobile banking may further specifically include the following contents before step 200, step 100 or step 010:

[0102] Step 020: Respectively receive and store the report platform login information, report search rules and historical RPA extraction rules for logging into the target bank's report platform; wherein the report search rules include the unique identifier of the enterprise mobile banking and the unique identifier of the business data report module; the historical RPA extraction rules include: extraction frequency, historical extraction start time, second time period, the unique identifier corresponding to each bank branch, and the extraction order of each bank branch;

[0103] Step 030: Automatically log in to the report platform of the target bank according to the report platform login information;

[0104] Step 040: Automatically search the business data report module of the enterprise mobile banking in the report platform based on the report search rule, wherein the business data report module contains the correspondence between bank outlets, enterprise mobile banking users and active user data;

[0105] Step 050: According to the historical RPA extraction rules, the historical corporate mobile banking user data corresponding to each of the bank branches in the second time period is extracted in the business data report module, wherein the historical corporate mobile banking user data includes active user data of at least one corporate mobile banking user corresponding to a bank branch.

[0106] From the above description, it can be seen that the corporate mobile banking user data processing method provided in the embodiment of the present application, by pre-setting the reporting platform login information, report search rules and historical RPA extraction rules, can effectively improve the automation level and efficiency of logging into the reporting platform of the target bank, automatically searching the business data reporting module of the corporate mobile banking in the reporting platform, and extracting the historical corporate mobile banking user data corresponding to each of the bank branches in the second time period in the business data reporting module.

[0107] In order to improve the accuracy and automation of determining the user activity change data of the enterprise mobile banking users corresponding to each of the bank branches, an embodiment of the enterprise mobile banking user data processing method provided in this application is provided. Figure 6 , step 200 in the enterprise mobile banking user data processing method specifically includes the following contents:

[0108] Step 210: Import the pre-acquired historical corporate mobile banking user data corresponding to each of the bank branches in the second time period and the target corporate mobile banking user data corresponding to the first time period into the summary table corresponding to each of the bank branches.

[0109] Step 220: Automatically obtain the target user activity of the corporate mobile banking users corresponding to each of the bank branches in the first time period based on the summary table, and obtain the historical user activity of the corporate mobile banking users corresponding to each of the bank branches in the second time period.

[0110] Step 230: Determine the user activity change data of the corporate mobile banking users corresponding to each of the bank branches according to the target user activity and historical user activity corresponding to each of the bank branches.

[0111] From the above description, it can be seen that the corporate mobile banking user data processing method provided in the embodiment of the present application can further improve the accuracy and automation of determining the user activity change data of the corporate mobile banking users corresponding to each of the bank branches in the first time period by automatically obtaining the target user activity of the corporate mobile banking users corresponding to each of the bank branches in the first time period based on the summary table, and obtaining the historical user activity of the corporate mobile banking users corresponding to each of the bank branches in the second time period.

[0112] In order to improve the timeliness and reliability of market operation and enterprise mobile banking product improvement according to the change of enterprise user activity, in one embodiment of the enterprise mobile banking user data processing method provided in this application, see Figure 7, the enterprise mobile banking user data processing method further specifically includes the following contents after step 200:

[0113] Step 300: Sort the bank branches in descending order according to the values ​​of the user activity change data to form a user activity ranking result.

[0114] Step 400: Output the user activity ranking result.

[0115] From the above description, it can be seen that the corporate mobile banking user data processing method provided in the embodiment of the present application can effectively improve the timeliness and reliability of market operations and corporate mobile banking product improvements based on changes in the activity of corporate users, and can effectively improve the user experience of the target bank. At the same time, it can also inform the connected bank branches based on the changes in the activity of corporate users to obtain timely and targeted feedback on the use of corporate mobile banking by their connected corporate users, thereby further improving the efficiency of operations such as functional optimization of corporate mobile banking, so as to simultaneously improve the user experience of corporate users using corporate mobile banking.

[0116] In order to improve the reliability and efficiency of obtaining active user data, in one embodiment of the corporate mobile banking user data processing method provided in the present application, the active user data in the corporate mobile banking user data processing method includes: the total number of users who log in to the corporate mobile banking via U-Shield or non-U-Shield within a preset time period and whose frequency exceeds a preset number of times.

[0117] From the above description, it can be seen that the corporate mobile banking user data processing method provided in the embodiment of the present application can effectively improve the reliability and efficiency of obtaining active user data by recording the total number of users whose frequency of logging into the corporate mobile banking via U-Shield or non-U-Shield within a preset time period exceeds a preset number of times as recorded by the internal system of the target bank.

[0118] From the software level, in order to solve the problem that the existing APP user data acquisition method cannot meet the efficiency and automation requirements of enterprise mobile banking user data processing, this application provides an embodiment of an RPA robot for executing all or part of the content of the enterprise mobile banking user data processing method, see Figure 8 , the RPA robot specifically includes the following contents:

[0119] The data extraction module 10 is used to automatically obtain the target enterprise mobile banking user data corresponding to each bank branch in the first time period from the report platform of the target bank based on the RPA extraction rules.

[0120] The data processing module 20 is used to determine the user activity change data of the corporate mobile banking users corresponding to each of the bank branches according to the pre-acquired historical corporate mobile banking user data corresponding to each of the bank branches in the second time period and the target corporate mobile banking user data corresponding to the first time period, wherein the second time period is earlier than the first time period.

[0121] The embodiment of the RPA robot provided in this application can be specifically used to execute the processing flow of the embodiment of the enterprise mobile banking user data processing method in the above embodiment. Its functions will not be repeated here, and reference can be made to the detailed description of the above method embodiment.

[0122] From the above description, it can be seen that the RPA robot provided in the embodiment of the present application directly extracts the operating data of the enterprise mobile banking from the internal system of the target bank, and takes the bank branch as the acquisition unit. In the process of acquiring the enterprise mobile banking user data, there is no need to communicate with third-party objects such as suppliers in real time, thereby effectively reducing the complexity of the enterprise mobile banking user data processing, effectively improving the efficiency of the enterprise mobile banking user data acquisition and reducing data redundancy; by adopting RPA extraction rules and the means of directly extracting the operating data of the enterprise mobile banking from the internal system of the target bank, combined with the method of taking the bank branch as the acquisition unit, the RPA technology can be applied to further improve the automation level of the enterprise mobile banking user data processing; by pre-acquired historical enterprise mobile banking user data corresponding to each of the bank branches in the second time period and in The target corporate mobile banking user data corresponding to each of the first time period can be used to determine the user activity change data of the corporate mobile banking users corresponding to each of the bank branches respectively. On the basis of obtaining the corporate mobile banking user data, the corporate user activity change status of the corporate mobile banking users can be intuitively and proactively obtained according to the user activity change data, thereby effectively improving the timeliness and reliability of market operations and corporate mobile banking product improvements based on the corporate user activity change status, and effectively improving the user experience of the target bank. At the same time, according to the corporate user activity change status, the connected bank branches can be informed of the corporate users' feedback on the use of corporate mobile banking in a timely and targeted manner, thereby further improving the efficiency of operations such as the function optimization of corporate mobile banking, so as to simultaneously improve the user experience of corporate users using corporate mobile banking.

[0123] In order to further illustrate this scheme, this application also provides a specific application example of a method for processing user data of enterprise mobile banking, which involves the field of RPA robotic process automation, and specifically involves a method for processing user data of enterprise mobile banking that automatically extracts the average monthly number of active accounts of enterprise mobile banking based on RPA robotic technology. The application example of this application is based on the artificial intelligence method of robotic process automation RPA, which automatically extracts the number of active accounts of enterprise mobile banking at all levels of institutions in each month before this month of this year and the data of institutions at all levels in the same period of the previous year, calculates the average monthly number of active accounts of institutions at all levels, and calculates the year-on-year growth of the average monthly number of active accounts this year and the average monthly number of active accounts last year to understand the completion of enterprise mobile banking of outlets in each institution, and evaluates the outlets of each institution every month through the calculated data. This method is automated, frees up manual operations, and avoids errors in the calculation process.

[0124] This application example uses RPA robotic process automation technology to extract the number of active accounts of corporate mobile banking and calculate the average number of active accounts per month. The process automation replaces manual operation. The process mainly includes two parts:

[0125] (I) Extracting data

[0126] When extracting data, you need to select one institution at a time and submit the monthly number of active accounts of the institution for download. This process is repetitive and requires a large amount of downloads. Assuming that you want to extract data from January to June and there are 8 institutions, you need to download 6×8=48 data reports, and you also need to download data reports for the same period of the previous year, for a total of 2×48=96 downloads. This process requires a large amount of operations and is labor-intensive. The enterprise mobile banking user data processing method provided in the application example of this application automates the process through RPA technology, greatly freeing up manpower and saving labor costs. In order to extract data from the previous year in the process, set a count=0 in RPA to count how many months have been extracted this year. The specific steps are as follows:

[0127] Step 11: RPA automatically logs in to the CS+ system (group reporting platform CS+) using the set username and password;

[0128] Step 12: RPA finds the enterprise mobile banking certificate version business status daily (monthly, quarterly, annual) report module in the system according to the set principles;

[0129] Step 13: Automatically set the frequency to monthly extraction;

[0130] Step 14: Automatically open the time and date and select January;

[0131] Step 15: Automatically open the organization and select an organization;

[0132] Step 16: Automatically submit the downloaded report and save it to the directory set in RPA. Repeat steps 15 and 16 to select other institutions until all institutions are selected. This will obtain the number of active accounts for each institution in January, and count will increase by 1.

[0133] Step 17: RPA opens the time date and selects the next month, and repeats steps 15 and 16 until the value of the month in this year is empty.

[0134] After obtaining the monthly active account report for all institutions this year, RPA automatically repeats steps 14-17 to obtain the active account report for the previous year. At the same time, the year is set to the previous year in the time and date selection, and the count in step 16 is changed to minus 1 until the count is 0 and the process ends.

[0135] The specific process of extracting data can be found in Fig. 9 , RPA automatically extracts the number of active accounts of each institution each month in this year and last year. In order to extract the data of last year conveniently, set count=0 in RPA to count the number of months extracted in this year.

[0136] (II) Data processing

[0137] For the data tables downloaded in the first part, if the number of active accounts of each branch in each data table is manually extracted from a large number of data tables, the operation is relatively frequent and prone to errors. In addition, manual calculation of the average monthly number of active accounts and year-on-year growth is also prone to errors. The enterprise mobile banking user data processing method provided in the application example of this application avoids data extraction and calculation errors through RPA technology. The specific steps are:

[0138] Step 21: RPA automatically extracts the number of active accounts of each institution’s outlets before this month according to the set rules and summarizes them into the EXCEL table of each institution set in RPA;

[0139] Step 22: RPA automatically calculates the total number of active customers and the average number of active customers per month for each institution’s outlets this year based on the summarized Excel spreadsheets of each institution;

[0140] Step 23: RPA automatically extracts the number of active accounts of each institution’s outlets during the same period of the previous year and summarizes them into the same EXCEL table of each institution in step 21;

[0141] Step 24: RPA automatically calculates the total number of active customers and the average number of active customers per month for each institution’s outlets in the previous year based on the summarized Excel spreadsheet;

[0142] Step 25: Automatically calculate (this year's average monthly number of active households - last year's average monthly number of active households / last year's average monthly number of active households) × 100% to get the year-on-year growth.

[0143] For the specific process of processing data, see Fig.10 , RPA automatically processes the downloaded report data and calculates the average monthly number of active accounts at all levels of institutional outlets and the year-on-year growth compared with the same period last year.

[0144] This application uses RPA technology to simulate the manual extraction of the number of active accounts of corporate mobile banking, calculate the average monthly number of active accounts and year-on-year growth, and free up manpower. This application automates the process of extracting the number of active accounts of corporate mobile banking, avoiding errors in calculating the average monthly number of active accounts and year-on-year growth.

[0145] Through RPA automatic login system, the number of active accounts of institutions at all levels before this month and the same period of last year is extracted according to the set principles and automatically saved to the set directory; then RPA automatically processes the downloaded report to obtain the average monthly number of active accounts of outlets at all levels of institutions and the year-on-year growth compared with the previous year. This method can be promoted to be used in fixed-rule and complex operation processes such as banks extracting assessment data for various professional lines.

[0146] From the hardware level, in order to solve the problem that the existing APP user data acquisition method cannot meet the efficiency and automation requirements of enterprise mobile banking user data processing, the present application provides an embodiment of an electronic device for implementing all or part of the content of the enterprise mobile banking user data processing method, and the electronic device specifically includes the following content:

[0147] Fig.11 FIG. 9 is a schematic block diagram of the system structure of the electronic device 9600 according to an embodiment of the present application. Fig.11 As shown, the electronic device 9600 may include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. It is worth noting that Fig.11 is exemplary; other types of structures may also be used to supplement or replace this structure to implement telecommunication functions or other functions.

[0148] In one embodiment, the enterprise mobile banking user data processing function may be integrated into the central processing unit. The central processing unit may be configured to perform the following control:

[0149] Step 100: Automatically obtain the target enterprise mobile banking user data corresponding to each bank branch in the first time period from the target bank's reporting platform based on the RPA extraction rules.

[0150] Step 200: Determine the user activity change data of the corporate mobile banking users corresponding to each of the bank branches respectively based on the pre-acquired historical corporate mobile banking user data corresponding to each of the bank branches in the second time period and the target corporate mobile banking user data corresponding to each of the first time period, wherein the second time period is earlier than the first time period.

[0151] From the above description, it can be seen that the electronic device provided in the embodiment of the present application directly extracts the operation data of the enterprise mobile banking from the internal system of the target bank, and takes the bank branch as the acquisition unit. In the process of acquiring the enterprise mobile banking user data, there is no need to communicate with third-party objects such as suppliers in real time, thereby effectively reducing the complexity of the enterprise mobile banking user data processing, effectively improving the efficiency of the enterprise mobile banking user data acquisition and reducing data redundancy; by adopting RPA extraction rules and the means of directly extracting the operation data of the enterprise mobile banking from the internal system of the target bank, combined with the method of taking the bank branch as the acquisition unit, RPA technology can be applied to further improve the automation level of the enterprise mobile banking user data processing; by pre-acquired historical enterprise mobile banking user data corresponding to each of the bank branches in the second time period and the By using the target corporate mobile banking user data corresponding to the first time period, the user activity change data of the corporate mobile banking users corresponding to each of the bank branches can be determined respectively, so that on the basis of obtaining the corporate mobile banking user data, the corporate user activity change situation of the corporate mobile banking users can be intuitively and proactively obtained according to the user activity change data, thereby effectively improving the timeliness and reliability of market operations and corporate mobile banking product improvements based on the corporate user activity change situation, and effectively improving the user experience of the target bank. At the same time, according to the corporate user activity change situation, the connected bank branches can be informed of the corporate users’ feedback on the use of corporate mobile banking in a timely and targeted manner, thereby further improving the efficiency of operations such as the function optimization of corporate mobile banking, so as to simultaneously improve the user experience of corporate users using corporate mobile banking.

[0152] In another embodiment, the RPA robot can be configured separately from the central processor 9100. For example, the RPA robot can be configured as a chip connected to the central processor 9100, and the enterprise mobile banking user data processing function can be realized through the control of the central processor.

[0153] like Fig.11 As shown, the electronic device 9600 may also include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is worth noting that the electronic device 9600 does not necessarily have to include Fig.11 In addition, the electronic device 9600 may also include Fig.11 For components not shown, reference may be made to the prior art.

[0154] like Fig.11As shown, the central processing unit 9100 is sometimes also referred to as a controller or an operation control, and may include a microprocessor or other processor device and / or logic device. The central processing unit 9100 receives input and controls the operation of various components of the electronic device 9600.

[0155] The memory 9140 may be, for example, one or more of a cache, a flash memory, a hard drive, a removable medium, a volatile memory, a non-volatile memory or other suitable devices. The above-mentioned information related to the failure may be stored, and a program for executing the relevant information may also be stored. The CPU 9100 may execute the program stored in the memory 9140 to implement information storage or processing, etc.

[0156] The input unit 9120 provides input to the central processing unit 9100. The input unit 9120 is, for example, a key or a touch input device. The power supply 9170 is used to provide power to the electronic device 9600. The display 9160 is used to display display objects such as images and texts. The display may be, for example, an LCD display, but is not limited thereto.

[0157] The memory 9140 may be a solid-state memory, such as a read-only memory (ROM), a random access memory (RAM), a SIM card, etc. It may also be a memory that saves information even when the power is off, can be selectively erased, and is provided with more data, examples of which are sometimes referred to as EPROMs, etc. The memory 9140 may also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application / function storage unit 9142, which is used to store application programs and function programs or processes for executing the operation of the electronic device 9600 through the central processor 9100.

[0158] The memory 9140 may also include a data storage unit 9143 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 9144 of the memory 9140 may include various drivers for communication functions of the electronic device and / or for executing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0159] The communication module 9110 is a transmitter / receiver 9110 that sends and receives signals via an antenna 9111. The communication module (transmitter / receiver) 9110 is coupled to the central processor 9100 to provide input signals and receive output signals, which may be the same as the case of a conventional mobile communication terminal.

[0160] Based on different communication technologies, multiple communication modules 9110 may be provided in the same electronic device, such as a cellular network module, a Bluetooth module and / or a wireless LAN module, etc. The communication module (transmitter / receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide an audio output via the speaker 9131 and receive an audio input from the microphone 9132, thereby realizing a common telecommunication function. The audio processor 9130 may include any suitable buffer, decoder, amplifier, etc. In addition, the audio processor 9130 is also coupled to the central processor 9100, so that recording can be performed on the local machine through the microphone 9132, and the sound stored on the local machine can be played through the speaker 9131.

[0161] The embodiments of the present application also provide a computer-readable storage medium capable of implementing all the steps of the enterprise mobile banking user data processing method in the above embodiments. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, all the steps of the enterprise mobile banking user data processing method in the above embodiments are implemented by the execution subject being a server or a client. For example, when the processor executes the computer program, the following steps are implemented:

[0162] Step 100: Automatically obtain the target enterprise mobile banking user data corresponding to each bank branch in the first time period from the target bank's reporting platform based on the RPA extraction rules.

[0163] Step 200: Determine the user activity change data of the corporate mobile banking users corresponding to each of the bank branches respectively based on the pre-acquired historical corporate mobile banking user data corresponding to each of the bank branches in the second time period and the target corporate mobile banking user data corresponding to each of the first time period, wherein the second time period is earlier than the first time period.

[0164] From the above description, it can be seen that the computer-readable storage medium provided in the embodiment of the present application directly extracts the operation data of the enterprise mobile banking from the internal system of the target bank, and takes the bank branch as the acquisition unit. In the process of acquiring the enterprise mobile banking user data, there is no need to communicate with third-party objects such as suppliers in real time, thereby being able to effectively improve the efficiency of enterprise mobile banking user data acquisition and reduce data redundancy on the basis of effectively reducing the complexity of enterprise mobile banking user data processing; by adopting RPA extraction rules and the means of directly extracting the operation data of the enterprise mobile banking from the internal system of the target bank, combined with the method of taking the bank branch as the acquisition unit, RPA technology can be applied to further improve the automation level of enterprise mobile banking user data processing; by pre-acquired historical enterprise mobile banking user data corresponding to each of the bank branches in the second time period, and the target corporate mobile banking user data corresponding to each of the first time period, and respectively determine the user activity change data of the corporate mobile banking users corresponding to each of the said bank outlets, so that on the basis of obtaining the corporate mobile banking user data, the corporate user activity change situation of the corporate mobile banking users can be intuitively and proactively obtained according to the user activity change data, thereby effectively improving the timeliness and reliability of market operations and corporate mobile banking product improvements based on the corporate user activity change situation, and effectively improving the user experience of the target bank. At the same time, according to the corporate user activity change situation, the connected bank outlets can be informed of the corporate user’s feedback on the use of corporate mobile banking in a timely and targeted manner, thereby further improving the efficiency of operations such as the function optimization of corporate mobile banking, so as to simultaneously improve the user experience of corporate users using corporate mobile banking.

[0165] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The present invention is described with reference to the flowcharts and / or block diagrams of the methods, devices (devices), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, may be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the functions in the process. Figure 1A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer-readable memory produce a product including an instruction device, which implements the functions specified in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0166] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0167] The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for processing enterprise mobile banking user data, characterized in that: include: Respectively receive and store the report platform login information, report search rules and target RPA extraction rules for logging into the target bank's report platform; wherein the report search rules include the unique identifier of the enterprise mobile banking and the unique identifier of the business data report module; the target RPA extraction rules include: extraction frequency, target extraction start time, first time period, the unique identifier corresponding to each bank branch, and the extraction order of each bank branch; Automatically obtain the target enterprise mobile banking user data corresponding to each bank branch in the first time period from the target bank's reporting platform based on RPA extraction rules; Determine the user activity change data of the corporate mobile banking users corresponding to each of the bank branches respectively according to the pre-acquired historical corporate mobile banking user data corresponding to each of the bank branches in the second time period and the target corporate mobile banking user data corresponding to each of the bank branches in the first time period, wherein the second time period is earlier than the first time period; The method of automatically obtaining the target enterprise mobile banking user data corresponding to each bank branch in the first time period from the report platform of the target bank based on the RPA extraction rule includes: Automatically log in to the reporting platform of the target bank according to the preset reporting platform login information; Automatically searching the business data report module of the enterprise mobile banking in the report platform based on a preset report search rule, wherein the business data report module contains the correspondence between bank branches, enterprise mobile banking users and active user data; According to the preset target RPA extraction rules, the target enterprise mobile banking user data corresponding to each of the bank branches within the first time period are extracted in the business data report module, wherein the target enterprise mobile banking user data includes active user data of at least one enterprise mobile banking user corresponding to a bank branch.

2. The method for processing enterprise mobile banking user data according to claim 1, characterized in that: Before determining the user activity change data of the enterprise mobile banking users corresponding to each of the bank outlets respectively according to the pre-acquired historical enterprise mobile banking user data corresponding to each of the bank outlets in the second time period and the target enterprise mobile banking user data corresponding to each of the first time period, the method further includes: Respectively receive and store the report platform login information, report search rules and historical RPA extraction rules for logging into the target bank's report platform; wherein the report search rules include the unique identifier of the enterprise mobile banking and the unique identifier of the business data report module; the historical RPA extraction rules include: extraction frequency, historical extraction start time, second time period, the unique identifier corresponding to each bank branch, and the extraction order of each bank branch; Automatically log in to the target bank's reporting platform according to the reporting platform login information; Automatically searching the business data report module of the enterprise mobile banking in the report platform based on the report search rule, wherein the business data report module contains the correspondence between bank branches, enterprise mobile banking users and active user data; According to the historical RPA extraction rules, the historical corporate mobile banking user data corresponding to each of the bank branches in the second time period are extracted in the business data reporting module, wherein the historical corporate mobile banking user data includes active user data of at least one corporate mobile banking user corresponding to a bank branch.

3. The method for processing enterprise mobile banking user data according to claim 1, characterized in that: Determining the user activity change data of the enterprise mobile banking users corresponding to each of the bank outlets respectively according to the pre-acquired historical enterprise mobile banking user data corresponding to each of the bank outlets in the second time period and the target enterprise mobile banking user data corresponding to each of the bank outlets in the first time period, includes: Importing the pre-acquired historical corporate mobile banking user data corresponding to each of the bank outlets in the second time period and the target corporate mobile banking user data corresponding to each of the first time period into the summary table corresponding to each of the bank outlets; Automatically obtain the target user activity of the enterprise mobile banking users corresponding to each of the bank outlets in the first time period based on the summary table, and obtain the historical user activity of the enterprise mobile banking users corresponding to each of the bank outlets in the second time period; The user activity change data of the corporate mobile banking users corresponding to each of the bank branches are determined respectively according to the target user activity and the historical user activity corresponding to each of the bank branches.

4. The method for processing enterprise mobile banking user data according to any one of claims 1 to 3, characterized in that: Also includes: Sorting the bank branches in descending order according to the values ​​of the user activity change data to form a user activity ranking result; Output the user activity ranking result.

5. The method for processing enterprise mobile banking user data according to any one of claims 1 to 2, characterized in that: The active user data includes: the total number of users who log in to the corporate mobile banking via USB shield or non-USB shield more than a preset number of times within a preset period of time.

6. An RPA robot, characterized in that: include: A data extraction module is used to respectively receive and store report platform login information, report search rules and target RPA extraction rules for logging into the target bank's report platform; wherein the report search rules include the unique identifier of the enterprise mobile banking and the unique identifier of the business data report module; the target RPA extraction rules include: extraction frequency, target extraction start time, first time period, the unique identifier corresponding to each bank branch, and the extraction order of each bank branch; based on the RPA extraction rules, the target enterprise mobile banking user data corresponding to each bank branch in the first time period is automatically obtained from the target bank's report platform; A data processing module, for determining the user activity change data of the enterprise mobile banking users corresponding to each of the bank outlets respectively according to the pre-acquired historical enterprise mobile banking user data corresponding to each of the bank outlets in a second time period and the target enterprise mobile banking user data corresponding to each of the bank outlets in the first time period, wherein the second time period is earlier than the first time period; Among them, the data extraction module is specifically used to automatically log in to the reporting platform of the target bank according to the preset reporting platform login information; based on the preset report search rules, automatically search the business data reporting module of the enterprise mobile banking in the reporting platform, wherein the business data reporting module contains the correspondence between bank branches, enterprise mobile banking users and active user data; according to the preset target RPA extraction rules, the target enterprise mobile banking user data corresponding to each of the bank branches within the first time period are respectively extracted in the business data reporting module, wherein the target enterprise mobile banking user data includes the active user data of at least one enterprise mobile banking user corresponding to a bank branch.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method for processing corporate mobile banking user data according to any one of claims 1 to 5 is implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for processing corporate mobile banking user data as described in any one of claims 1 to 5 is implemented.

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