Rpa robot tag adjustment method and apparatus

Abstract

The application discloses a kind of RPA robot label adjustment method and device, wherein the method comprises: according to the single task processing duration of RPA robot, task quantity, the actual quantity of RPA robot, determine the supply difference of RPA robot;According to the time period that RPA robot utilization is lower than preset value, determine the execution time range of RPA robot;According to the supply difference of RPA robot, determine the RPA robot demand state of label;According to the execution time range of RPA robot and the RPA robot demand state of label, determine dynamic label;The effective time period of setting dynamic label, in the effective time period of dynamic label, according to RPA robot demand state, dynamically adjust dynamic label.The application can improve RPA robot processing efficiency, alleviate relevant task queuing waiting problem.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of human-computer interaction automation, and in particular to an RPA robot label adjustment method and device. BACKGROUND

[0002] This section is intended to provide background or context to the embodiments of the application recited in the claims. The description herein is not admitted to be prior art merely by inclusion in this section.

[0003] In recent years, RPA has been rising and popular year by year. At present, RPA robot application matching and scheduling schemes are developed and implemented by each manufacturer independently. The existing robot device scheduling adopts pre-set labels, and the device time-sharing scheduling task and optimization strategy are limited to grouping within fixed labels. Thus, different device groups are separated from each other, idle device groups cannot be fully utilized, and some label devices have a high utilization rate during working hours and are idle during non-working hours. SUMMARY

[0004] The embodiments of the present application provide an RPA robot label adjustment method to improve the processing efficiency of RPA robots and alleviate the queuing problem of related tasks. The method comprises:

[0005] According to the single task processing time of the RPA robot, the number of tasks, and the actual number of RPA robots, the supply difference of the RPA robot is determined.

[0006] According to the time period when the utilization rate of the RPA robot is lower than the preset value, the execution time range of the RPA robot is determined.

[0007] According to the supply difference of the RPA robot, the RPA robot demand state of the label is determined.

[0008] According to the execution time range of the RPA robot and the RPA robot demand state of the label, a dynamic label is determined.

[0009] The effective time period of the dynamic label is set, and in the effective time period of the dynamic label, the dynamic label is dynamically adjusted according to the RPA robot demand state.

[0010] The embodiments of the present application also provide an RPA robot label adjustment device to improve the processing efficiency of RPA robots and alleviate the queuing problem of related tasks. The device comprises:

[0011] The supply difference of the RPA robot is determined by the supply difference determination module of the RPA robot according to the single task processing time of the RPA robot, the number of tasks, and the actual number of RPA robots.

[0012] The RPA robot execution time range determination module is configured to determine an execution time range of the RPA robot according to a time period during which the utilization rate of the RPA robot is lower than a preset value.

[0013] The RPA robot demand state determination module of the label is configured to determine an RPA robot demand state of the label according to the supply difference of the RPA robot.

[0014] The dynamic label determination module is configured to determine a dynamic label according to the execution time range of the RPA robot and the RPA robot demand state of the label.

[0015] The dynamic label dynamic adjustment module is configured to set an effective time period of the dynamic label, and dynamically adjust the dynamic label according to the RPA robot demand state within the effective time period of the dynamic label.

[0016] The embodiment of the application further provides a computer device, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, and the processor implements the RPA robot label adjustment method when executing the computer program.

[0017] The embodiment of the application further provides a computer readable storage medium, which stores a computer program, and the computer program implements the RPA robot label adjustment method when executed by a processor.

[0018] The embodiment of the application further provides a computer program product, which comprises a computer program, and the computer program implements the RPA robot label adjustment method when executed by a processor.

[0019] Compared with the prior art in which labels are preset, RPA robot time scheduling tasks and optimization strategies are limited to RPA robot grouping in fixed labels, in the embodiment of the application, the supply difference of the RPA robot is determined according to the single task processing time of the RPA robot, the number of tasks, and the actual number of RPA robots; the execution time range of the RPA robot is determined according to a time period during which the utilization rate of the RPA robot is lower than a preset value; the RPA robot demand state of the label is determined according to the supply difference of the RPA robot; the dynamic label is determined according to the execution time range of the RPA robot and the RPA robot demand state of the label; and the effective time period of the dynamic label is set, and the dynamic label is dynamically adjusted according to the RPA robot demand state within the effective time period of the dynamic label. In the above process, the dynamic label is adjusted according to the supply difference of the RPA robot and the execution time range of the RPA robot, so that the RPA robot is no longer limited to fixed labels, and rapid and reasonable adjustment of the RPA robot is made in a timely manner, thereby improving the utilization rate and processing efficiency of the RPA robot. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor. In the drawings:

[0021] Figure 1 a flow chart of the RPA robot tag adjustment method in the embodiment of the present application;

[0022] Figure 2 a principle diagram of the RPA robot tag adjustment method in the embodiment of the present application;

[0023] Figure 3 a schematic diagram of the RPA robot tag adjustment device in the embodiment of the present application;

[0024] Figure 4 a schematic diagram of another RPA robot tag adjustment device in the embodiment of the present application;

[0025] Figure 5 a schematic diagram of another RPA robot tag adjustment device in the embodiment of the present application;

[0026] Figure 6 a schematic diagram of another RPA robot tag adjustment device in the embodiment of the present application. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solutions and advantages of the embodiments of the present application more clear, the embodiments of the present application will be further described in detail below in combination with the drawings. Herein, the illustrative embodiments of the present application and their descriptions are used to explain the present application, but not as a limitation of the present application.

[0028] Figure 1 a flow chart of the RPA robot tag adjustment method in the embodiment of the present application, Figure 2 a principle diagram of the RPA robot tag adjustment method in the embodiment of the present application, the present application adjusts the dynamic tag according to the supply difference of the RPA robot and the execution time range of the RPA robot, so as not to be limited to the fixed tag, and timely makes a quick and reasonable adjustment to the RPA robot, improves the utilization rate and processing efficiency of the RPA robot, and the method comprises:

[0029] Step 101, determining the supply difference of the RPA robot according to the single task processing time of the RPA robot, the task quantity, and the actual quantity of the RPA robot;

[0030] Step 102, according to the time period in which the utilization rate of the RPA robot is lower than the preset value, determining the execution time range of the RPA robot;

[0031] Step 103, according to the supply difference of the RPA robot, determining the RPA robot demand state of the label;

[0032] Step 104, according to the execution time range of the RPA robot and the RPA robot demand state of the label, determining the dynamic label;

[0033] Step 105, setting the effective time period of the dynamic label, and in the effective time period of the dynamic label, dynamically adjusting the dynamic label according to the RPA robot demand state.

[0034] Each step will be described in detail below. First, the following calculations need to be made in advance:

[0035] According to the statistical rules, the number of devices corresponding to each label is counted to obtain the actual number of devices; the historical business volume is predicted to obtain the task quantity; and the historical total task processing time is divided by the historical total task quantity to obtain the single task processing time.

[0036] In an embodiment, the actual number of devices is obtained by counting the number of devices corresponding to each label according to the statistical rules, including:

[0037] When the same device has N labels, the number of devices corresponding to each label is set to 1 / N, and the equivalent number of devices corresponding to each label is the actual number of devices.

[0038] In specific embodiments, the average business volume of the last four weeks is taken to predict the weekly task quantity, and the same method is used to predict the monthly task quantity according to (this year's last month's business volume / last year's last month's business volume)*last year's last month's business volume.

[0039] In step 101, according to the single task processing time of the RPA robot, the task quantity, and the actual number of RPA robots, the supply difference of the RPA robot is determined.

[0040] In an embodiment, the supply difference of the RPA robot is determined according to the single task processing time of the RPA robot, the task quantity, and the actual number of RPA robots, including:

[0041] According to the single task processing time of the RPA robot and the task quantity, the required number of RPA robots is determined;

[0042] According to the difference between the actual number of RPA robots and the required number, the supply difference of the RPA robot is determined.

[0043] In specific embodiments, the supply difference value process of the RPA robot is as follows:

[0044] (1) Actual RPA robot quantity: Since there are cases where the same device has multiple labels, when the same device has N labels, the number of RPA robots corresponding to each label is set to 1 / N, and the equivalent RPA robot quantity corresponding to each label, i.e. the actual RPA robot quantity.

[0045] (2) Task quantity: i.e. the predicted business volume.

[0046] (3) Single processing duration: total processing duration of each application by report divided by the total number of applications.

[0047] (4) Required actual RPA robot quantity: single processing duration * task quantity / 10 (hours).

[0048] (5) Supply difference of RPA robot: actual RPA robot quantity-required actual RPA robot quantity.

[0049] In step 102, according to the time period when the utilization rate of the RPA robot is lower than the preset value, the execution time range of the RPA robot is determined.

[0050] In an embodiment, it further includes:

[0051] According to the historical utilization rate distribution of the RPA robot and the task quantity, the time period when the utilization rate of the RPA robot is lower than the preset value is predicted.

[0052] In step 103, according to the supply difference of the RPA robot, the demand state of the RPA robot of the label is determined.

[0053] In an embodiment, according to the supply difference of the RPA robot, the demand state of the RPA robot of the label is determined, including:

[0054] When the supply difference of the RPA robot is negative, the demand state of the RPA robot of the label is insufficient, and when the supply difference of the RPA robot is positive, the demand state of the RPA robot of the label is idle.

[0055] In specific embodiments, according to the business volume prediction and the historical utilization rate distribution, the RPA robot execution time range prediction is obtained. The following is the calculation rule of the RPA robot execution range prediction:

[0056] (1) In order to ensure that high-priority tasks are not waiting (i.e. RPA robots are dedicated to executing high-priority tasks and do not participate in dynamic tag adjustment), a coefficient is needed to determine how many RPA robots in the group can adjust the dynamic tag according to the coefficient. The coefficient is calculated based on the historical execution of RPA robots with high-priority tags and the predicted business volume. RPA robots that will not participate in dynamic tag adjustment are marked for subsequent adjustment.

[0057] (2) According to the historical utilization rate distribution of RPA robots and the task volume prediction, the time period with utilization rate lower than a certain percentage (such as 98%) is calculated.

[0058] In step 104, the dynamic tag is determined according to the execution time range of the RPA robot and the RPA robot demand state of the tag.

[0059] In an embodiment, the dynamic tag is determined according to the execution time range of the RPA robot and the RPA robot demand state of the tag, comprising:

[0060] The tag with the RPA robot demand state as idle is set as the dynamic tag.

[0061] In a specific embodiment, the supply difference of the RPA robot and the execution time range of the RPA robot are obtained according to the capacity planning algorithm. Table 1 shows the capacity prediction of each tag.

[0062] Table 1 Capacity prediction of each tag

[0063]

[0064] As shown in Table 1, the supply difference of the RPA robot for the FLASH tag is negative, indicating that the RPA robot demand state is insufficient, while the supply difference of the RPA robot for the remaining tags is positive, indicating that the RPA robot demand state is idle. Therefore, the tag with the RPA robot demand state as idle can be set as the dynamic tag, so that the RPA robot can be quickly and reasonably adjusted in time, improving the utilization rate and processing efficiency of the RPA robot.

[0065] In step 105, the effective time period of the dynamic tag is set. Within the effective time period of the dynamic tag, the dynamic tag is dynamically adjusted according to the RPA robot demand state.

[0066] In an embodiment, within the effective time period of the dynamic tag, the dynamic tag is dynamically adjusted according to the RPA robot demand state, comprising:

[0067] The dynamic tag is dynamically adjusted to supplement the tag with the RPA robot demand state as insufficient.

[0068] In an embodiment, further comprising:

[0069] After the effective time of the dynamic label ends, the original label is restored.

[0070] In a specific embodiment, the dynamic adjustment steps of the dynamic label are as follows:

[0071] (1) Obtain the supply difference value of the RPA robot derived from the capacity planning algorithm.

[0072] (2) According to the negative supply difference value of the RPA robot, it is necessary to increase the RPA robot because the RPA robot is insufficient; if the supply difference value of the RPA robot is positive, the label corresponding to the RPA robot can be set to a dynamic label because the RPA robot is idle.

[0073] (3) According to the time period predicted by the execution range of the RPA robot, set the effective time of the dynamic label.

[0074] (4) After the setting is successful, the label corresponding to the RPA robot is dynamically adjusted according to the set time to the label with a negative supply difference value.

[0075] (5) After the label adjustment time ends, the original label is restored.

[0076] According to the operation of the RPA robot, the utilization rate of the RPA robot can basically reach more than 90% during the working hours 8:00-18:00, while most of the RPA robots are idle during the night 18:00-6:00 the next day. In order to fully utilize this part of time, the above-mentioned method can be used to set the time period as the effective time period of the dynamic label.

[0077] In an embodiment of the application, an RPA robot label adjustment device is also provided, as described in the following embodiment. Since the principle of solving the problem of the device is similar to that of the RPA robot label adjustment method, the implementation of the device can be referred to the implementation of the RPA robot label adjustment method, and the repeated parts will not be described again. As shown in Figure 3 The schematic diagram of the RPA robot label adjustment device in an embodiment of the application is shown in the figure, which comprises:

[0078] The supply difference value determination module 301 of the RPA robot is used to determine the supply difference value of the RPA robot according to the single task processing time of the RPA robot, the number of tasks, and the actual number of RPA robots.

[0079] The execution time range determination module 302 of the RPA robot is used to determine the execution time range of the RPA robot according to the time period during which the utilization rate of the RPA robot is lower than the preset value.

[0080] The RPA robot demand state determination module 303 of the label is used for determining the RPA robot demand state of the label according to the supply difference value of the RPA robot.

[0081] The dynamic label determination module 304 is used for determining a dynamic label according to the execution time range of the RPA robot and the RPA robot demand state of the label.

[0082] The dynamic label dynamic adjustment module 305 is used for setting an effective time period of the dynamic label, and dynamically adjusting the dynamic label according to the RPA robot demand state in the effective time period of the dynamic label.

[0083] In an embodiment, the supply difference value determination module 301 of the RPA robot is used for:

[0084] determining the required number of RPA robots according to the single task processing time length and the task quantity of the RPA robot;

[0085] determining the supply difference value of the RPA robot according to the difference between the actual number and the required number of the RPA robot.

[0086] As shown in FIG. 1, the RPA robot label adjustment device includes a supply difference value determination module 301, a dynamic label determination module 304, and a dynamic label dynamic adjustment module 305. Figure 4 As shown in FIG. 1, the RPA robot label adjustment device includes a supply difference value determination module 301, a dynamic label determination module 304, and a dynamic label dynamic adjustment module 305.

[0087] predicting a time period in which the utilization rate of the RPA robot is lower than a preset value according to the historical utilization rate distribution and the task quantity of the RPA robot.

[0088] In an embodiment, the RPA robot demand state determination module 303 of the label is used for:

[0089] when the supply difference value of the RPA robot is a negative value, the RPA robot demand state of the label is insufficient, and when the supply difference value of the RPA robot is positive, the RPA robot demand state of the label is idle.

[0090] In an embodiment, the dynamic label determination module 304 is used for:

[0091] setting the label with the idle RPA robot demand state as the dynamic label.

[0092] In an embodiment, the dynamic label dynamic adjustment module 305 is used for:

[0093] dynamically adjusting the dynamic label to supply the label with the insufficient RPA robot demand state.

[0094] As shown in FIG. 1, the RPA robot label adjustment device includes a supply difference value determination module 301, a dynamic label determination module 304, and a dynamic label dynamic adjustment module 305. Figure 5As shown is a schematic view of another two RPA robot tag adjustment devices in the embodiment of the present application, in an embodiment, further comprising a data acquisition module 501, used for:

[0095] According to the statistical rules, the number of devices corresponding to each tag is counted to obtain the actual number of devices;

[0096] According to the historical business volume, the weekly and monthly business volume is predicted to obtain the task quantity;

[0097] According to the historical total task processing time divided by the historical total task number, the single task processing time is obtained.

[0098] In an embodiment, the data acquisition module 501, used for:

[0099] When the same device has N tags, the number of devices corresponding to each tag is set to 1 / N, and the equivalent number of devices corresponding to each tag is the actual number of devices.

[0100] As shown Figure 6 As shown is a schematic view of another three RPA robot tag adjustment devices in the embodiment of the present application, in an embodiment, further comprising a recovery module 601, used for:

[0101] After the effective time of the dynamic tag ends, the original tag is restored.

[0102] The embodiment of the present application also provides a computer device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to realize the RPA robot tag adjustment method.

[0103] The embodiment of the present application also provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize the RPA robot tag adjustment method.

[0104] The embodiment of the present application also provides a computer program product, which comprises a computer program, and the computer program is executed by a processor to realize the RPA robot tag adjustment method.

[0105] Compared with the prior art that adopts pre-set labels, the RPA robot divides the tasks and optimizes the strategy in time, and the RPA robot grouping is limited in the fixed labels, in the embodiment of the application, the supply difference of the RPA robot is determined according to the single task processing time length of the RPA robot, the task quantity, and the actual quantity of the RPA robot; the execution time range of the RPA robot is determined according to the time period in which the utilization rate of the RPA robot is lower than a preset value; the RPA robot demand state of the label is determined according to the supply difference of the RPA robot; the dynamic label is determined according to the execution time range of the RPA robot and the RPA robot demand state of the label; and the effective time period of the dynamic label is set, and the dynamic label is dynamically adjusted according to the RPA robot demand state in the effective time period of the dynamic label. In the above process, the dynamic label is adjusted according to the supply difference of the RPA robot and the execution time range of the RPA robot, so that the RPA robot is no longer limited to the fixed label, and the RPA robot can be quickly and reasonably adjusted in time, and the utilization rate and processing efficiency of the RPA robot are improved.

[0106] Those skilled in the art will understand that embodiments of the application can be provided as methods, systems, or computer program products. Therefore, the application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the application can 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-ROMs, optical storage, etc.) containing computer-usable program code.

[0107] The application is described with reference to flowcharts and / or block diagrams of the method, device (system), and computer program product according to the embodiments of the application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to produce a machine, so that the instructions executed by the computer or other programmable data processing devices generate a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that carries out the functions specified in one or more flows and / or blocks.

[0108] These computer program instructions can also be stored in a computer-readable memory that can guide the computer or other programmable data processing devices to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including instruction apparatus, which implements the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1the function specified in the one or more blocks.

[0109] These computer program instructions can also be loaded into computer or other programmable data processing devices, so that a series of operational steps are performed on the computer or other programmable data processing devices to generate computer-implemented processes, thus the instructions executed on the computer or other programmable data processing devices provide processes for implementing the flows Figure 1 the flow or flows and / or blocks Figure 1 the steps of the function specified in the one or more blocks.

[0110] The above-described specific embodiments, the purpose, technical solutions and beneficial effects of the present application are further described in detail, it should be understood that the above-described is only a specific embodiment of the present application, and is not used to limit the protection scope of the present application, any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for RPA robot tag adjustment, characterized in that, The method comprises the following steps: determining the supply difference of the RPA robots according to the single-task processing time of the RPA robots, the task quantity, and the actual quantity of the RPA robots; determining the execution time range of the RPA robots according to the time period during which the utilization rate of the RPA robots is lower than a preset value; determining the demand state of the RPA robots with labels according to the supply difference of the RPA robots; determining the dynamic label according to the execution time range of the RPA robots and the demand state of the RPA robots with labels; setting the effective time period of the dynamic label, and dynamically adjusting the dynamic label according to the demand state of the RPA robots within the effective time period of the dynamic label; determining the supply difference of the RPA robots according to the single-task processing time of the RPA robots, the task quantity, and the actual quantity of the RPA robots, which comprises the following steps: determining the required quantity of the RPA robots according to the single-task processing time and the task quantity of the RPA robots; determining the supply difference of the RPA robots according to the difference between the actual quantity and the required quantity of the RPA robots; determining the dynamic label according to the execution time range of the RPA robots and the demand state of the RPA robots with labels, which comprises the following steps: setting the label of the idle demand state of the RPA robots as the dynamic label; dynamically adjusting the dynamic label, and supplementing the label of the insufficient demand state of the RPA robots within the effective time period of the dynamic label. The method further comprises the following steps: statistically determining the actual quantity of the RPA robots corresponding to each label according to a statistical rule; predicting the task quantity according to the historical business volume; obtaining the single-task processing time according to the historical total task processing time divided by the historical total task quantity. The method further comprises the following steps:

2. The method of claim 1, wherein, predicting the time period during which the utilization rate of the RPA robots is lower than a preset value according to the historical utilization rate distribution of the RPA robots and the task quantity. determining the demand state of the RPA robots with labels according to the supply difference of the RPA robots, which comprises the following steps:

3. The method of claim 1, wherein, when the supply difference of the RPA robots is a negative value, the demand state of the RPA robots is insufficient; and when the supply difference of the RPA robots is a positive value, the demand state of the RPA robots is idle. statistically determining the actual quantity of the RPA robots corresponding to each label according to a statistical rule, which comprises the following steps:

4. The method of claim 1, wherein, when the same RPA robot has N labels, the quantity of the RPA robots corresponding to each label is set as 1 / N, and the equivalent quantity of the RPA robots corresponding to each label is the actual quantity of the RPA robots. The method further comprises the following steps:

5. The method of claim 1, wherein, restoring the original label after the effective time of the dynamic label ends. The method comprises the following steps:

6. An RPA robot tag adjustment apparatus, characterized by, a supply difference determination module, configured to determine the supply difference of the RPA robots according to the single-task processing time of the RPA robots, the task quantity, and the actual quantity of the RPA robots; an execution time range determination module, configured to determine the execution time range of the RPA robots according to the time period during which the utilization rate of the RPA robots is lower than a preset value; ​ The RPA robot demand state determination module of the label is configured to determine the demand state of the RPA robot with the label according to the supply difference of the RPA robot. The dynamic label determination module is configured to determine a dynamic label according to the execution time range of the RPA robot and the demand state of the RPA robot with the label. The dynamic label dynamic adjustment module is configured to set an effective time period of the dynamic label, and dynamically adjust the dynamic label according to the demand state of the RPA robot within the effective time period of the dynamic label. The supply difference determination module of the RPA robot is configured to: determine the required number of RPA robots according to the single-task processing time of the RPA robot and the task quantity; and determine the supply difference of the RPA robot according to the difference between the actual number and the required number of RPA robots. The dynamic label determination module is configured to: set the label with the idle demand state of the RPA robot as the dynamic label. The dynamic label dynamic adjustment module is configured to: dynamically adjust the dynamic label to supplement the label with the insufficient demand state of the RPA robot. The data acquisition module is further configured to: statistically acquire the actual number of RPA robots corresponding to each label according to statistical rules; predict the weekly and monthly business volumes according to historical business volumes to obtain the task quantity; and obtain the single-task processing time according to the historical total task processing time divided by the historical total number of tasks.

7. The apparatus of claim 6, wherein, The prediction module is further configured to: predict the time period in which the utilization rate of the RPA robot is lower than a preset value according to the historical utilization rate distribution of the RPA robot and the task quantity.

8. The apparatus of claim 6, wherein, The RPA robot demand state determination module of the label is configured to: when the supply difference of the RPA robot is negative, the demand state of the RPA robot is insufficient, and when the supply difference of the RPA robot is positive, the demand state of the RPA robot is idle.

9. The apparatus of claim 6, wherein, The data acquisition module is configured to: when the same RPA robot has N labels, the number of RPA robots corresponding to each label is set to 1 / N, and the equivalent number of RPA robots corresponding to each label is the actual number of RPA robots.

10. The apparatus of claim 6, wherein, The recovery module is further configured to: restore the original label after the effective time of the dynamic label ends.

11. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the method of any one of claims 1 to 5.

12. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the method of any one of claims 1 to 5.

13. A computer program product, characterised in that, The computer program product comprises a computer program, and the computer program is executed by the processor to implement the method of any one of claims 1 to 5.

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