RPA operation result abnormity early warning method, device, equipment and medium

Abstract

The invention provides an RPA operation result abnormity early warning method and device, equipment and a medium, and the method comprises the steps: obtaining RPA operation data set on the current day at an operation background, the operation data comprising an operation success number, an operation failure number and an error type; deleting data, known as an error type of a user side, in the operation data to obtain operation correction data, and calculating a success rate of the day; comparing the success rate of the day with the success rate of the same RPA in each day in the week, and judging whether the RPA operates normally or not; if the RPA runs normally, the RPA is not processed; if the RPA operates abnormally, abnormal information of the RPA is written into a set position, and the abnormal information comprises an operation log and an abnormal screenshot; a notification is sent to a set person; the troubleshooting difficulty of developers is reduced, and the overall working efficiency of enterprises is improved.

Description

Technical Field

[0001] The present invention relates to the field of abnormal warning technology, and in particular to a method, device, equipment and medium for warning abnormality of RPA operation results. Background Art

[0002] Existing companies provide online RPA with multiple functions. Users can use the required RPA after logging in online. However, there are some failure scenarios when RPA is executing tasks. The existing troubleshooting method is that the operation personnel first check the failed tasks in the operation background and submit the corresponding information to the developer to troubleshoot the cause. This is a long and time-consuming process.

[0003] Relying on manpower to check is time-consuming and laborious, and some failed tasks are caused by the user side, such as: the account provided by the user has expired, the site entered by the user does not exist, etc. If you check one by one, it will undoubtedly consume a lot of manpower and time, greatly reducing work efficiency. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a method, device, equipment and medium for warning abnormality of RPA operation results, which effectively solves the problem that the PRA troubleshooting process is long, time-consuming and labor-intensive, and reduces the workload of developers.

[0005] In a first aspect, the present invention provides a method for warning abnormal RPA operation results, comprising the following steps:

[0006] Step 1: Obtain the RPA operation data set for the day in the operation background. The operation data includes: the number of successful operations, the number of failed operations, and the error type.

[0007] Step 2: Delete the data of the error type known to the user in the operation data, obtain the operation correction data, and calculate the success rate of the day;

[0008] Step 3: Compare the success rate of the day with the success rate of the same RPA model every day last week.

[0009] If the success rate of the day is lower than the lowest success rate of the previous week and the offset is greater than the first threshold, the RPA is judged to be operating abnormally;

[0010] If the success rate on the current day is lower than the lowest success rate last week and the offset is less than or equal to the first threshold, the RPA is determined to be operating normally;

[0011] If the success rate on the current day is lower than the success rate of a certain day last week, and is greater than or equal to the lowest success rate last week, and the offset is less than or equal to the second threshold, then the RPA operation data is compared with the operation data of the previous day. If there is a new error type, the RPA is judged to be operating abnormally; if there is no new error type, the RPA is judged to be operating normally;

[0012] If the success rate on the current day is lower than the success rate on a certain day last week, and is greater than or equal to the lowest success rate last week, and the offset is greater than the second threshold, the RPA is determined to be operating abnormally;

[0013] If the success rate on the current day is greater than or equal to the success rate of the same RPA model on each day last week, the RPA model is considered to be operating normally.

[0014] Step 4: If the RPA is running normally, no processing is performed; if the RPA is running abnormally, the abnormal information of the RPA is written to the set location, and the abnormal information includes the operation log and the abnormal screenshot; and a notification is sent to the setting personnel.

[0015] In a second aspect, the present invention provides an RPA operation result abnormality warning device, comprising:

[0016] The data acquisition module acquires the RPA operation data set for the day in the operation background. The operation data includes: the number of successful operations, the number of failed operations, and the error type;

[0017] The data calculation module deletes the data of the error type known to the user in the operation data, obtains the operation correction data, and calculates the success rate of the day;

[0018] The comparison and judgment module compares the success rate of the day with the success rate of the same RPA model every day last week.

[0019] If the success rate of the day is lower than the lowest success rate of the previous week and the offset is greater than the first threshold, the RPA is judged to be operating abnormally;

[0020] If the success rate on the current day is lower than the lowest success rate last week and the offset is less than or equal to the first threshold, the RPA is determined to be operating normally;

[0021] If the success rate on the current day is lower than the success rate of a certain day last week, and is greater than or equal to the lowest success rate last week, and the offset is less than or equal to the second threshold, then the RPA operation data is compared with the operation data of the previous day. If there is a new error type, the RPA is judged to be operating abnormally; if there is no new error type, the RPA is judged to be operating normally;

[0022] If the success rate on the current day is lower than the success rate on a certain day last week, and is greater than or equal to the lowest success rate last week, and the offset is greater than the second threshold, the RPA is determined to be operating abnormally;

[0023] If the success rate on the current day is greater than or equal to the success rate of the same RPA model on each day last week, the RPA model is considered to be operating normally.

[0024] The early warning module does not process the RPA if it is operating normally; if it is operating abnormally, the abnormal information of the RPA is written to the set location, including the operation log and abnormal screenshot; and a notification is sent to the setting personnel.

[0025] In a third aspect, the present invention 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 method described in the first aspect when executing the program.

[0026] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect.

[0027] One or more technical solutions provided by the present invention have at least the following technical effects or advantages:

[0028] The technical solution of the present invention stores all abnormal data according to the set ID, which can be easily and quickly found; and if the abnormal situation is a false alarm or is caused by factors such as network delay, these factors are excluded to ensure that the RPA running abnormally is not a false alarm, which greatly reduces the workload of developers.

[0029] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The present invention will be further described below in conjunction with embodiments with reference to the accompanying drawings.

[0031] Figure 1 is a flow chart of the method in Embodiment 1 of the present invention;

[0032] Figure 2 It is a schematic diagram of the structure of the device in the second embodiment of the present invention. DETAILED DESCRIPTION

[0033] The embodiments of the present application provide a method, device, equipment and medium for warning abnormality of RPA operation results to solve the problem that the PRA troubleshooting process is long, time-consuming and labor-intensive, and may cause mistakes.

[0034] The technical solution in the embodiment of the present application has the following general idea:

[0035] By writing Python scripts, the automatic monitoring function of RPA operation results is realized. In the operation background, obtain the first N (N is a positive integer) RPAs in terms of running quantity, obtain the running success rate of these RPAs, and compare the success rate of the PRA task today with the success rate of the previous week to see if the success rate has decreased by more than 5%. If it exceeds 5%, all error results corresponding to the RPA and the error log of the corresponding scheduling background are uniformly recorded in the local Excel table. If it does not exceed 5%, only the newly added error types (compared with yesterday) are recorded in the local Excel table. If there are no newly added error types, it is determined that the RPA is running normally. After collecting the running results, they are automatically sent to the group chat through DingTalk so that developers can troubleshoot the problems.

[0036] Embodiment 1

[0037] like Figure 1 As shown, this embodiment provides an RPA operation result abnormal warning method, including the following steps:

[0038] Step 1: Obtain the RPA operation data set for the day in the operation background. The operation data includes: the number of successful operations, the number of failed operations, and the error type;

[0039] Step 2: Delete the data of the error type known to the user in the operation data, obtain the operation correction data, and calculate the success rate of the day;

[0040] Step 3: Compare the success rate of the day with the success rate of the same RPA model every day last week.

[0041] If the success rate of the day is lower than the lowest success rate of the previous week and the offset is greater than the first threshold, the RPA is judged to be operating abnormally;

[0042] If the success rate on the current day is lower than the lowest success rate last week and the offset is less than or equal to the first threshold, the RPA is determined to be operating normally;

[0043] If the success rate on the current day is lower than the success rate of a certain day last week, and is greater than or equal to the lowest success rate last week, and the offset is less than or equal to the second threshold, then the RPA operation data is compared with the operation data of the previous day. If there is a new error type, the RPA is judged to be operating abnormally; if there is no new error type, the RPA is judged to be operating normally;

[0044] If the success rate on the current day is lower than the success rate on a certain day last week, and is greater than or equal to the lowest success rate last week, and the offset is greater than the second threshold, the RPA is determined to be operating abnormally;

[0045] If the success rate on the current day is greater than or equal to the success rate of the same RPA model on each day last week, the RPA model is considered to be operating normally.

[0046] Step 4: If the RPA is running normally, no processing is performed; if the RPA is running abnormally, the abnormal information of the RPA is written to the set location, and the abnormal information includes the operation log and the abnormal screenshot; and a notification is sent to the setting personnel.

[0047] In this embodiment, preferably, step 3 is specifically: comparing the success rate X of the day with the success rate of the same RPA model every day last week,

[0048] If the success rate of the day is lower than the lowest success rate Y1 of the previous week and the offset Z1 is greater than the first threshold, Z1 = (Y1-X) / X, then the RPA is judged to be operating abnormally;

[0049] If the success rate of the day is lower than the lowest success rate of the previous week and the offset Z1 is less than or equal to the first threshold, the RPA is determined to be operating normally;

[0050] If the success rate on the current day is lower than the success rate of a certain day last week, and is greater than or equal to the lowest success rate last week, and the offset is less than or equal to the second threshold, then the RPA operation data is compared with the operation data of the previous day. If there is a new error type, the RPA is judged to be operating abnormally; if there is no new error type, the RPA is judged to be operating normally;

[0051] If the success rate on the current day is lower than the success rate on a certain day last week, and is greater than or equal to the lowest success rate last week, and the offset is greater than the second threshold, the RPA is determined to be operating abnormally;

[0052] If the success rate on the current day is greater than or equal to the success rate of the same RPA model on each day last week, the RPA model is considered to be operating normally.

[0053] The second threshold is greater than the first threshold; the first threshold is 5%.

[0054] In this embodiment, preferably, the step 1 is specifically as follows: obtaining the operation data of the RPA set for the day in the operation background, each RPA includes multiple execution tasks; the operation data includes: task operation log, number of successful operations, number of failed operations and error type; setting robotID for RPA and runID for execution task; making the robotID correspond to the runID; and storing the task operation log according to the runID and robotID.

[0055] In this embodiment, preferably, step 4 is specifically as follows: if the RPA is running normally, no processing is performed; if the RPA is running abnormally, the corresponding task running log and the abnormal information of the RPA are written into the set location according to the robotID and runID, and the abnormal information includes the running log and the abnormal screenshot; and a notification is sent to the setting personnel.

[0056] Based on the same inventive concept, the present application also provides a device corresponding to the method in Example 1, see Example 2 for details.

[0057] Embodiment 2

[0058] like Figure 2 As shown, in this embodiment, a device for early warning of abnormal RPA operation results is provided, including:

[0059] The data acquisition module acquires the RPA operation data set for the day in the operation background. The operation data includes: the number of successful operations, the number of failed operations, and the error type.

[0060] The data calculation module deletes the data of the error type known to the user in the operation data, obtains the operation correction data, and calculates the success rate of the day;

[0061] The comparison and judgment module compares the success rate of the day with the success rate of the same RPA model every day last week.

[0062] If the success rate of the day is lower than the lowest success rate of the previous week and the offset is greater than the first threshold, the RPA is judged to be operating abnormally;

[0063] If the success rate on the current day is lower than the lowest success rate last week and the offset is less than or equal to the first threshold, the RPA is determined to be operating normally;

[0064] If the success rate on the current day is lower than the success rate of a certain day last week, and is greater than or equal to the lowest success rate last week, and the offset is less than or equal to the second threshold, then the RPA operation data is compared with the operation data of the previous day. If there is a new error type, the RPA is judged to be operating abnormally; if there is no new error type, the RPA is judged to be operating normally;

[0065] If the success rate on the current day is lower than the success rate on a certain day last week, and is greater than or equal to the lowest success rate last week, and the offset is greater than the second threshold, the RPA is determined to be operating abnormally;

[0066] If the success rate on the current day is greater than or equal to the success rate of the same RPA model on each day last week, the RPA model is considered to be operating normally.

[0067] The early warning module does not process the RPA if it is operating normally; if it is operating abnormally, the abnormal information of the RPA is written to the set location, including the operation log and abnormal screenshot; and a notification is sent to the setting personnel.

[0068] In this embodiment, preferably, the comparison and judgment module specifically comprises: comparing the success rate X of the day with the success rate of the same RPA model on each day of the previous week,

[0069] If the success rate of the day is lower than the lowest success rate Y1 of the previous week and the offset Z1 is greater than the first threshold, Z1 = (Y1-X) / X, then the RPA is judged to be operating abnormally;

[0070] If the success rate of the day is lower than the lowest success rate of the previous week and the offset Z1 is less than or equal to the first threshold, the RPA is determined to be operating normally;

[0071] If the success rate on the current day is lower than the success rate of a certain day last week, and is greater than or equal to the lowest success rate last week, and the offset is less than or equal to the second threshold, then the RPA operation data is compared with the operation data of the previous day. If there is a new error type, the RPA is judged to be operating abnormally; if there is no new error type, the RPA is judged to be operating normally;

[0072] If the success rate on the current day is lower than the success rate on a certain day last week, and is greater than or equal to the lowest success rate last week, and the offset is greater than the second threshold, the RPA is determined to be operating abnormally;

[0073] If the success rate on the current day is greater than or equal to the success rate of the same RPA model on each day last week, the RPA model is considered to be operating normally.

[0074] The second threshold is greater than the first threshold; the first threshold is 5%.

[0075] In this embodiment, preferably, the data acquisition module is specifically as follows: obtaining the operation data of the RPA set for the day in the operation background, each RPA includes multiple execution tasks; the operation data includes: task operation log, number of successful operations, number of failed operations and error type; setting robotID for RPA and runID for execution task; matching the robotID with runID; storing the task operation log according to runID and robotID.

[0076] In this embodiment, preferably, the early warning module is specifically as follows: if the RPA is operating normally, no processing is performed; if the RPA is operating abnormally, the corresponding task operation log and the abnormal information of the RPA are written into the set location according to the robotID and runID, and the abnormal information includes the operation log and the abnormal screenshot; and a notification is sent to the setting personnel.

[0077] Since the device introduced in the second embodiment of the present invention is a device used to implement the method of the first embodiment of the present invention, those skilled in the art can understand the specific structure and deformation of the device based on the method introduced in the first embodiment of the present invention, so it is not described here in detail. All devices used in the method of the first embodiment of the present invention belong to the scope of protection of the present invention.

[0078] Based on the same inventive concept, the present application provides an electronic device embodiment corresponding to the first embodiment, see the third embodiment for details.

[0079] Embodiment 3

[0080] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, any implementation method in the first embodiment can be implemented.

[0081] Since the electronic device introduced in this embodiment is a device used to implement the method in the first embodiment of the present application, based on the method introduced in the first embodiment of the present application, a person skilled in the art can understand the specific implementation of the electronic device of the present embodiment and its various variations, so how the electronic device implements the method in the embodiment of the present application is not described in detail here. As long as a person skilled in the art implements the device used by the method in the embodiment of the present application, it belongs to the scope of protection of the present application.

[0082] Based on the same inventive concept, the present application provides a storage medium corresponding to the first embodiment, see the fourth embodiment for details.

[0083] Embodiment 4

[0084] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, any implementation method in the first embodiment can be implemented.

[0085] The technical solution provided in the embodiments of the present application has at least the following technical effects or advantages:

[0086] This implementation method effectively solves the problem that the PRA troubleshooting process is long, time-consuming and labor-intensive, and reduces the workload of developers.

[0087] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, 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. 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 code.

[0088] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0089] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0090] 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.

[0091] Although the specific implementation modes of the present invention are described above, those skilled in the art should understand that the specific implementation modes described are only illustrative and are not intended to limit the scope of the present invention. Equivalent modifications and changes made by those skilled in the art in accordance with the spirit of the present invention should be included in the scope of protection of the claims of the present invention.

Claims

1. A method for early warning of abnormal RPA operation results, characterized by: The steps include: Step 1: Obtain the RPA operation data set for the day in the operation background. The operation data includes: the number of successful operations, the number of failed operations, and the error type. Step 2: Delete the data of the error type known to the user in the operation data, obtain the operation correction data, and calculate the success rate of the day; Step 3: Compare the success rate of the day with the success rate of the same RPA model every day last week. If the success rate of the day is lower than the lowest success rate of the previous week and the offset is greater than the first threshold, the RPA is judged to be operating abnormally; If the success rate on the current day is lower than the lowest success rate last week and the offset is less than or equal to the first threshold, the RPA is determined to be operating normally; If the success rate on the current day is lower than the success rate of a certain day last week, and is greater than or equal to the lowest success rate last week, and the offset is less than or equal to the second threshold, then the RPA operation data is compared with the operation data of the previous day. If there is a new error type, the RPA is judged to be operating abnormally; if there is no new error type, the RPA is judged to be operating normally; If the success rate on the current day is lower than the success rate on a certain day last week, and is greater than or equal to the lowest success rate last week, and the offset is greater than the second threshold, the RPA is determined to be operating abnormally; If the success rate on the current day is greater than or equal to the success rate of the same RPA model on each day last week, the RPA model is considered to be operating normally. Step 4: If the RPA is running normally, no processing is performed; if the RPA is running abnormally, the abnormal information of the RPA is written to the set location, and the abnormal information includes the operation log and the abnormal screenshot; and a notification is sent to the setting personnel.

2. According to claim 1, the method for early warning of abnormal RPA operation results is characterized by: The step 3 is specifically as follows: compare the success rate X of the day with the success rate of the same RPA model every day last week. If the success rate of the day is lower than the lowest success rate Y1 of the previous week and the offset Z1 is greater than the first threshold, Z1 = (Y1-X) / X, then the RPA is judged to be operating abnormally; If the success rate of the day is lower than the lowest success rate of the previous week and the offset Z1 is less than or equal to the first threshold, the RPA is determined to be operating normally; If the success rate on the current day is lower than the success rate of a certain day last week, and is greater than or equal to the lowest success rate last week, and the offset is less than or equal to the second threshold, then the RPA operation data is compared with the operation data of the previous day. If there is a new error type, the RPA is judged to be operating abnormally; if there is no new error type, the RPA is judged to be operating normally; If the success rate on the current day is lower than the success rate on a certain day last week, and is greater than or equal to the lowest success rate last week, and the offset is greater than the second threshold, the RPA is determined to be operating abnormally; If the success rate on the current day is greater than or equal to the success rate of the same RPA model on each day last week, the RPA model is considered to be operating normally. The second threshold is greater than the first threshold; the first threshold is 5%.

3. According to claim 1, the method for early warning of abnormal RPA operation results is characterized by: The step 1 is specifically as follows: obtaining the operation data of the RPA set for the day in the operation background, each RPA includes multiple execution tasks; the operation data includes: task operation log, number of successful operations, number of failed operations and error type; setting robotID for RPA and runID for execution task; matching the robotID with runID; storing the task operation log according to runID and robotID.

4. According to claim 3, the method for early warning of abnormal RPA operation results is characterized by: The specific step 4 is as follows: if the RPA is running normally, no processing is performed; if the RPA is running abnormally, the corresponding task running log and the abnormal information of the RPA are written into the set location according to the robotID and runID, and the abnormal information includes the running log and the abnormal screenshot; and a notification is sent to the setting personnel.

5. An RPA operation result abnormal warning device, characterized by: include: The data acquisition module acquires the RPA operation data set for the day in the operation background. The operation data includes: the number of successful operations, the number of failed operations, and the error type. The data calculation module deletes the data of the error type known to the user in the operation data, obtains the operation correction data, and calculates the success rate of the day; The comparison and judgment module compares the success rate of the day with the success rate of the same RPA model every day last week. If the success rate of the day is lower than the lowest success rate of the previous week and the offset is greater than the first threshold, the RPA is judged to be operating abnormally; If the success rate on the current day is lower than the lowest success rate last week and the offset is less than or equal to the first threshold, the RPA is determined to be operating normally; If the success rate on the current day is lower than the success rate of a certain day last week, and is greater than or equal to the lowest success rate last week, and the offset is less than or equal to the second threshold, then the RPA operation data is compared with the operation data of the previous day. If there is a new error type, the RPA is judged to be operating abnormally; if there is no new error type, the RPA is judged to be operating normally; If the success rate on the current day is lower than the success rate on a certain day last week, and is greater than or equal to the lowest success rate last week, and the offset is greater than the second threshold, the RPA is determined to be operating abnormally; If the success rate on the current day is greater than or equal to the success rate of the same RPA model on each day last week, the RPA model is considered to be operating normally. The early warning module does not process the RPA if it is operating normally; if it is operating abnormally, the abnormal information of the RPA is written to the set location, including the operation log and abnormal screenshot; and a notification is sent to the setting personnel.

6. The RPA operation result abnormal warning device according to claim 5 is characterized by: The comparison and judgment module specifically compares the success rate X of the day with the success rate of the same RPA model every day last week. If the success rate of the day is lower than the lowest success rate Y1 of the previous week and the offset Z1 is greater than the first threshold, Z1 = (Y1-X) / X, then the RPA is judged to be operating abnormally; If the success rate of the day is lower than the lowest success rate of the previous week and the offset Z1 is less than or equal to the first threshold, the RPA is determined to be operating normally; If the success rate on the current day is lower than the success rate of a certain day last week, and is greater than or equal to the lowest success rate last week, and the offset is less than or equal to the second threshold, then the RPA operation data is compared with the operation data of the previous day. If there is a new error type, the RPA is judged to be operating abnormally; if there is no new error type, the RPA is judged to be operating normally; If the success rate on the current day is lower than the success rate on a certain day last week, and is greater than or equal to the lowest success rate last week, and the offset is greater than the second threshold, the RPA is determined to be operating abnormally; If the success rate on the current day is greater than or equal to the success rate of the same RPA model on each day last week, the RPA model is considered to be operating normally. The second threshold is greater than the first threshold; the first threshold is 5%.

7. The RPA operation result abnormal warning device according to claim 5, characterized in that: The data acquisition module is specifically as follows: obtaining the operation data of the RPA set for the day in the operation background, each RPA includes multiple execution tasks; the operation data includes: task operation log, number of successful operations, number of failed operations and error type; setting robotID for RPA and runID for execution task; matching the robotID with runID; storing the task operation log according to runID and robotID.

8. The RPA operation result abnormal warning device according to claim 7 is characterized by: The early warning module is specifically as follows: if the RPA is running normally, no processing will be done; if the RPA is running abnormally, the corresponding task running log and the abnormal information of the RPA will be written into the set location according to the robotID and runID, and the abnormal information includes the running log and the abnormal screenshot; and a notification will be sent to the setting personnel.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 4 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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