Task scheduling method, device, equipment and storage medium based on RPA and AI

Abstract

The present application proposes a task scheduling method, apparatus, device and storage medium based on RPA and AI, the method comprising: determining the target RPA robot group to be granted concurrent authorization number and the authorization type of the target RPA robot group; according to the authorization type of the target RPA robot group, setting the target concurrent authorization number corresponding to the target RPA robot group for the target RPA robot group, so that when the authorization type of the target RPA robot group is the lowest authorization type, the number of RPA tasks that the target RPA robot group can execute concurrently is controlled to be at least the target concurrent authorization number of the lowest authorization type, and when the authorization type of the target RPA robot group is the highest authorization type, the number of RPA tasks that the target RPA robot group can execute concurrently is controlled to be the target concurrent authorization number. Based on AI technology, the present application ensures that all task execution permissions will not be occupied by some RPA robot groups all the time, so that all types of RPA tasks can be executed, thereby improving task execution efficiency.

Description

Technical Field

[0001] The present application relates to the field of robotic process automation technology, and in particular to a task scheduling method, device, equipment and storage medium based on RPA and AI. Background Art

[0002] Robotic Process Automation (RPA) uses specific "robot software" to simulate human operations on computers and automatically execute process tasks according to rules.

[0003] Artificial Intelligence (AI) is a technical science that studies and develops theories, methods, technologies and application systems for simulating, extending and expanding human intelligence.

[0004] The RPA platform includes an RPA control center and at least one RPA robot. The RPA control center is responsible for managing all RPA robots in the RPA platform and RPA task scheduling. Common task scheduling models include task push model, task pull model, and task push-pull combined model.

[0005] The following describes the task scheduling process for these three models:

[0006] (I) Task push model: When the RPA control center generates a new task, it directly pushes the new task to the specified RPA robot according to the task type of the new task. However, for the distributed RPA platform, since there are multiple layers of middle-tier servers and edge servers between the RPA control center and the RPA robot, the RPA control center cannot accurately detect the idle or busy status of the RPA robot, which may cause the specified RPA robot to be busy and unable to process the new task, thereby reducing the success rate of task push.

[0007] (2) Task pull model: Each RPA robot directly pulls new tasks from the RPA control center. However, when the RPA robot sends a pull request to the RPA control center, the RPA control center generates new tasks, and the task pull success rate will increase. When the RPA robot sends a pull request to the RPA control center, the RPA control center does not generate new tasks, and the task pull success rate will decrease. In particular, when the RPA control center has no new tasks for a long time, the RPA robot will poll and perform invalid task pull operations (which can be referred to as empty polling), which is a great waste of resources.

[0008] (III) Task push-pull combination model: The task push-pull combination model is a combination of the task push model and the task pull model. When the RPA control center generates a new task, it pushes a task pull notification to the RPA robot group responsible for the task type according to the task type of the new task. After the RPA robot group receives the task pull notification, the idle RPA robots in the RPA robot group will pull the task from the RPA control center, thereby avoiding multiple empty polling.

[0009] Although the task push-pull combination model solves the defects of the task push model and the task pull model, it still has the problem that some RPA robot groups always occupy the rights to execute RPA tasks, which causes other RPA robot groups to be unable to process tasks in a timely manner. Assume that business system X that has purchased RPA services has RPA robot groups A and B on the RPA platform, and RPA robot group A needs to execute a large number of tasks at a high frequency, while RPA robot group B has a lower execution frequency but is very important. Since RPA robot group A has a high execution frequency and a large number of tasks, the RPA control center may initially authorize the RPA task execution rights authorized to business system X to RPA robot group A. When RPA robot group B needs to execute, it may be that all RPA task execution rights are occupied by RPA robot group A, causing RPA robot group B to be unable to execute tasks for a long time, or even cause task execution failure due to exceeding the deadline of the task that RPA robot group B is responsible for. Summary of the invention

[0010] The embodiments of the present application provide a task scheduling method, apparatus, device and storage medium based on RPA and AI to solve the problem in the related art that all RPA task execution permissions are occupied by some RPA robot groups, resulting in RPA tasks that other RPA robot groups need to execute being difficult to execute for a long time. The technical solution is as follows:

[0011] In a first aspect, an embodiment of the present application provides a task scheduling method based on RPA and AI, which is applied to an RPA control center and includes:

[0012] S1. Determine the target Robotic Process Automation (RPA) robot group and the authorization type of the target RPA robot group to be granted the number of concurrent authorizations, wherein the authorization type includes a minimum authorization type and / or a maximum authorization type;

[0013] S2. According to the authorization type of the target RPA robot group, a target concurrent authorization number corresponding to the target RPA robot group is set for the target RPA robot group, so that when the authorization type of the target RPA robot group is the lowest authorization type, the target RPA robot group of the lowest authorization type is controlled to have at least the number of RPA tasks that can be concurrently executed equal to the target concurrent authorization number corresponding to the target RPA robot group of the lowest authorization type; when the authorization type of the target RPA robot group is the highest authorization type, the target RPA robot group of the highest authorization type is controlled to have at most the number of RPA tasks that can be concurrently executed equal to the target concurrent authorization number corresponding to the target RPA robot group of the highest authorization type, wherein the target concurrent authorization number is less than the total concurrent authorization number of the business system.

[0014] In one embodiment, S2 includes:

[0015] S21. Obtain the authorized time period of the target RPA robot group, where the authorized time period is the time period during which the target RPA robot has the authority to execute the RPA task;

[0016] S22. Determine the authorization time for the target RPA robot group to be authorized according to the start time of the authorization time period and the task running time prediction model, wherein the task running time prediction model is a neural network model trained according to the historical RPA task running conditions and used to predict the future running time of the RPA task, and the authorization time is less than or equal to the start time of the authorization time period;

[0017] S23. When the authorization time arrives, according to the authorization type of the target RPA robot group, a target concurrent authorization number is set for the target RPA robot group within the authorization time period, so that when the authorization type of the target RPA robot group is the lowest authorization type, the target RPA robot group of the lowest authorization type is controlled to have at least the target concurrent authorization number of tasks that can be executed concurrently within the authorization time period equal to the target concurrent authorization number corresponding to the target RPA robot group of the lowest authorization type; when the authorization type of the target RPA robot group is the highest authorization type, the target RPA robot group of the highest authorization type is controlled to have at most the target concurrent authorization number of tasks that can be executed concurrently within the authorization time period equal to the target concurrent authorization number corresponding to the target RPA robot group of the highest authorization type.

[0018] In one embodiment, S22 includes:

[0019] S221, determining a prediction time for prediction using a task runtime prediction model according to the start time of the authorized time period, wherein the prediction time is less than the authorized time;

[0020] S222: When the predicted time arrives, input task information of at least one running RPA task into a task running time prediction model to obtain the future running time of at least one running RPA task, wherein the task information includes the task running start time and the task completion degree;

[0021] S223. Determine an authorization time for authorizing the target RPA robot group according to the future running time of at least one running RPA task.

[0022] In one implementation, S223 includes:

[0023] S2231. Calculate the average future running time of at least one running RPA task;

[0024] S2232: Determine the time corresponding to the difference between the start time of the authorized time period and the average value as the authorized time.

[0025] In one embodiment, the historical RPA task running status includes the authorization waiting time for each RPA robot to obtain authorization for the responsible RPA task, the authorization success rate for each RPA robot to obtain authorization for the responsible RPA task, and the task running time of each RPA task.

[0026] In one embodiment, S1 includes:

[0027] S11. Group the RPA robots corresponding to the RPA tasks whose execution frequency is less than the first preset frequency threshold, and determine them as the target RPA robot group of the lowest authorization type;

[0028] And / or, S12, grouping the RPA robots corresponding to the RPA tasks having an execution frequency greater than a second preset frequency threshold, and determining them as the target RPA robot group of the highest authorization type;

[0029] The first preset frequency threshold is smaller than the second preset frequency threshold.

[0030] In one embodiment, before S2, the method further includes:

[0031] S3. Determine the target concurrent authorization number according to the number of RPA tasks to be executed by the target RPA robot group within an execution cycle and the total concurrent authorization number of the business system.

[0032] In a second aspect, an embodiment of the present application provides a task scheduling device based on RPA and AI, the method is applied to an RPA control center, and the device includes:

[0033] A determination unit, configured to determine a target RPA robot group to which a concurrent authorization number is to be granted and an authorization type of the target RPA robot group, wherein the authorization type includes a minimum authorization type and / or a maximum authorization type;

[0034] A setting unit is used to set a target concurrent authorization number corresponding to the target RPA robot group for the target RPA robot group according to the authorization type of the target RPA robot group, so that when the authorization type of the target RPA robot group is the lowest authorization type, the target RPA robot group of the lowest authorization type is controlled to have at least the number of RPA tasks that can be concurrently executed to be the target concurrent authorization number corresponding to the target RPA robot group of the lowest authorization type; when the authorization type of the target RPA robot group is the highest authorization type, the target RPA robot group of the highest authorization type is controlled to have at most the number of RPA tasks that can be concurrently executed to be the target concurrent authorization number corresponding to the target RPA robot group of the highest authorization type, wherein the target concurrent authorization number is less than the total concurrent authorization number of the business system.

[0035] In one embodiment, the setting unit includes:

[0036] An acquisition module, used to acquire the authorized time period of the target RPA robot group, wherein the authorized time period is the time period during which the target RPA robot has the authority to execute the RPA task;

[0037] An authorization time determination module is used to determine the authorization time for the target RPA robot group to be authorized based on the start time of the authorization time period and the task running time prediction model, wherein the task running time prediction model is a neural network model trained based on the historical RPA task running conditions and used to predict the future running time of the RPA task, and the authorization time is less than or equal to the start time of the authorization time period;

[0038] A setting module is used to set a target concurrent authorization number for the target RPA robot group within the authorization time period according to the authorization type of the target RPA robot group when the authorization time arrives, so that when the authorization type of the target RPA robot group is the lowest authorization type, the target RPA robot group of the lowest authorization type is controlled to have at least the target concurrent authorization number of tasks that can be executed concurrently within the authorization time period, and when the authorization type of the target RPA robot group is the highest authorization type, the target RPA robot group of the highest authorization type is controlled to have at most the target concurrent authorization number of tasks that can be executed concurrently within the authorization time period, and the target RPA robot group of the highest authorization type is controlled to have the target concurrent authorization number of tasks that can be executed concurrently within the authorization time period.

[0039] In one embodiment, the authorization time determination module includes:

[0040] A first determination submodule is used to determine a prediction time for prediction using a task runtime prediction model according to a start time of the authorization time period, wherein the prediction time is less than the authorization time;

[0041] A prediction submodule, configured to input task information of at least one running RPA task into a task running time prediction model when a prediction time is reached, and obtain a future running time of at least one running RPA task, wherein the task information includes a task running start time and a task completion degree;

[0042] The second determination submodule is used to determine the authorization time for authorizing the target RPA robot group according to the future running time of at least one running RPA task.

[0043] In one embodiment, the second determination submodule is used to calculate the average future running time of at least one running RPA task, and determine the time corresponding to the difference between the start time of the authorized time period and the average value as the authorized time.

[0044] In one embodiment, the historical RPA task running status includes the authorization waiting time for each RPA robot to obtain authorization for the responsible RPA task, the authorization success rate for each RPA robot to obtain authorization for the responsible RPA task, and the task running time of each RPA task.

[0045] In one embodiment, the determination unit includes a first group determination module and / or a second group determination module;

[0046] A first group determination module is used to group the RPA robots corresponding to the RPA tasks whose execution frequency is less than a first preset frequency threshold, and determine them as the target RPA robot group of the lowest authorization type;

[0047] A second group determination module is used to group the RPA robots corresponding to the RPA tasks whose execution frequency is greater than the second preset frequency threshold, and determine them as the target RPA robot group of the highest authorization type;

[0048] The first preset frequency threshold is smaller than the second preset frequency threshold.

[0049] In one embodiment, the determination unit is further used to determine the target concurrent authorization number based on the number of RPA tasks to be executed by the target RPA robot group within an execution cycle and the total concurrent authorization number of the business system.

[0050] In a third aspect, an embodiment of the present application provides an electronic device, the electronic device comprising a processor and a memory, the memory storing instructions, the instructions being loaded and executed by the processor to implement a method in any one of the above-mentioned aspects.

[0051] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the method in any one of the above-mentioned embodiments is implemented.

[0052] The advantages or beneficial effects of the above technical solution include at least:

[0053] 1. The embodiment of the present application allocates the lowest target concurrent authorization number to the target RPA robot group with the lowest authorization type, so that even if most of the concurrent authorization numbers are occupied by other RPA robot groups, the target robot process automation robot group with the lowest authorization type can at least concurrently execute the target concurrent authorization number of RPA tasks, and / or, by allocating the highest target concurrent authorization number to the target RPA robot group with the highest authorization type, even if most of the concurrent authorization numbers are occupied by the target RPA robot group with the highest authorization type, there are still remaining concurrent authorization numbers for other RPA robot groups to use. It can be seen from this that the embodiment of the present application can avoid the problem that all RPA task execution permissions (i.e., the total concurrent authorization number of execution permissions) are occupied by some RPA robot groups, resulting in the RPA tasks that other RPA robot groups need to execute being difficult to execute for a long time, thereby improving the execution efficiency and success rate of RPA tasks in the business system as a whole.

[0054] 2. By setting an authorization time period for the target RPA robot group, the target RPA robot group can have at least or at most the target authorized concurrent RPA task execution authority only during the authorization time period, thereby avoiding the problem that the authorized concurrent number is occupied due to the target RPA robot group not having to execute tasks outside the authorization time period, so that other RPA tasks cannot be processed in time. In addition, in order to prevent the problem that the released concurrent authorization number cannot reach the target concurrent authorization number when the target concurrent authorization number is set for the target RPA robot group at the start time of the authorization time period because other RPA tasks have not been completed, the embodiment of the present application can first determine the authorization time that is less than or equal to the start time of the authorization time period according to the start time of the authorization time period and the task running time prediction model, and set the target concurrent authorization number for the target RPA robot group when the authorization time is reached, so as to ensure that the target RPA robot group can have the target concurrent authorization number within the authorization time period.

[0055] 3. After determining the predicted time by the start time of the authorization time period, when the predicted time is reached, the future running time of at least one running RPA task can be predicted based on the task running time prediction model, and the authorization time for the target RPA robot group can be determined according to the future running time. This can ensure that the number of concurrent authorizations that the target RPA robot group can have within the authorization time period is the target concurrent authorization number, and as many other RPA tasks as possible can be completed before the authorization time period.

[0056] 4. By grouping the RPA robots corresponding to the RPA tasks with an execution frequency less than the first preset frequency threshold as the target RPA robot group of the lowest authorization type, and grouping the RPA robots corresponding to the RPA tasks with an execution frequency greater than the second preset frequency threshold as the target RPA robot group of the highest authorization type, both high-frequency RPA tasks and low-frequency RPA tasks can be successfully completed on the basis of reasonably occupying the RPA task execution authority. In addition, the target concurrent authorization number can be determined based on the number of RPA tasks to be executed by the target RPA robot group within an execution cycle and the total concurrent authorization number of the business system, thereby further improving the utilization rate of the RPA task execution authority.

[0057] The above summary is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features of the present application will be readily apparent by reference to the accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In the accompanying drawings, unless otherwise specified, the same reference numerals throughout the multiple drawings represent the same or similar parts or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings only depict some embodiments disclosed in the present application and should not be regarded as limiting the scope of the present application.

[0059] Figure 1 A flowchart of a task scheduling method based on RPA and AI provided in an embodiment of the present application;

[0060] Figure 2 A flowchart of another task scheduling method based on RPA and AI provided in an embodiment of the present application;

[0061] Figure 3 An example diagram of a task scheduling method based on RPA and AI provided in an embodiment of the present application;

[0062] Figure 4 A block diagram of a task scheduling device based on RPA and AI provided in an embodiment of the present application;

[0063] Figure 5 An architecture diagram of an electronic device provided in an embodiment of the present application;

[0064] Figure 6 An architectural diagram of another electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0065] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be understood as limiting the present application.

[0066] In the description of the embodiments of the present application, the term "RPA", i.e., Robotic Process Automation, is a process that automatically executes process tasks according to rules by simulating human operations on computers through specific "robot software". The term "RPA robot group" includes at least one RPA robot, and different RPA robots in the same RPA robot group have the same characteristics, and RPA robots in different RPA robot groups have different characteristics. For example, RPA robots can be divided into multiple RPA robot groups according to the RPA task type, and different RPA robot groups are used to perform different types of RPA tasks.

[0067] In the description of the embodiments of the present application, the term "concurrent authorization number" refers to the number of permissions granted to concurrently execute PRA tasks. The term "authorization type" is an attribute of an RPA robot group, mainly including a minimum authorization type and / or a maximum authorization type, wherein the minimum authorization type refers to the attribute of an RPA robot group that needs to be granted the minimum concurrent authorization number, and the maximum authorization type refers to the attribute of an RPA robot group that needs to be granted the highest concurrent authorization number. The term "authorization time period" refers to the time period in which the target RPA robot has the permission to execute PRA tasks. The term "authorization time" refers to the time when the PRA control center authorizes the target RPA robot group.

[0068] In the description of the embodiments of the present application, the term "task running time prediction model" is a neural network model trained based on the historical RPA task running conditions and used to predict the future running time of RPA tasks. Among them, "historical RPA task running conditions" include the authorization waiting time for each RPA robot to obtain authorization for the responsible RPA task, the authorization success rate of each RPA robot to obtain authorization for the responsible RPA task, and the task running time of each RPA task. The authorization waiting time refers to the time required from the RPA robot receiving the task pull notification issued by the RPA control center to successfully pulling the RPA task. The authorization success rate refers to the ratio of the number of successful times to the total number of times the RPA robot pulls the RPA task from the RPA control center after receiving the RPA task pull notification within the preset historical time period.

[0069] In the description of the embodiments of the present application, the term "regression model" is a predictive modeling technique that can study the relationship between dependent variables and independent variables. Therefore, the above-mentioned "task runtime prediction model" can specifically be a "regression model" that can predict the future runtime of RPA tasks.

[0070] In the description of the embodiments of the present application, the term "business system" refers to a system that has purchased at least one RPA robot in the RPA platform and needs the RPA robot to handle its business.

[0071] In the description of the embodiments of the present application, the term "OCR" refers to optical character recognition, which specifically refers to the process in which an electronic device examines characters printed on paper, determines their shapes by detecting dark and light patterns, and then translates the shapes into computer text using character recognition methods; that is, for printed characters, an optical method is used to convert the text in a paper document into a black and white dot matrix image file, and recognition software is used to convert the text in the image into a text format for further editing and processing by word processing software.

[0072] In the description of the embodiments of the present application, the term "NLP" refers to Natural Language Processing, which is a discipline that uses computer technology to analyze, understand and process natural language with language as the object. That is, it uses computers as a powerful tool for language research, conducts quantitative research on language information with the support of computers, and provides language descriptions that can be used by both humans and computers.

[0073] These and other aspects of the embodiments of the present application will be clear with reference to the following description and drawings. In these descriptions and drawings, some specific implementations in the embodiments of the present application are specifically disclosed to represent some ways of implementing the principles of the embodiments of the present application, but it should be understood that the scope of the embodiments of the present application is not limited thereto. On the contrary, the embodiments of the present application include all changes, modifications and equivalents that fall within the spirit and connotation of the appended claims.

[0074] RPA technology can intelligently understand the existing applications of electronic devices through the user interface, and automate repetitive, rule-based, and large-scale routine operations, such as automatically and repeatedly reading emails, reading Office components, operating databases and web pages, client software, etc., collecting data and performing tedious calculations, and generating the required files and reports in batches. RPA technology can greatly reduce the investment in labor costs and effectively improve office efficiency. AI technology can break through fixed rules and simulate human thinking and consciousness to automatically process some more complex application scenarios. RPA has unique advantages: low code and non-intrusive. Low code means that RPA does not require a high level of IT to operate, and business personnel who do not understand programming can also develop processes; non-intrusive means that RPA can simulate human operations without the need for software system open interfaces. However, traditional RPA has certain limitations: it can only be based on fixed rules and has limited application scenarios. With the continuous development of AI technology, the deep integration of RPA and AI has overcome the limitations of traditional RPA. RPA+AI=Hand work+Head work is greatly changing the value of labor. Based on this, an embodiment of the present application provides a method for implementing task scheduling by combining RPA and AI technologies, which can solve the problem that all RPA task execution permissions have been occupied by some RPA robot groups, resulting in RPA tasks that other RPA robot groups need to execute being difficult to execute for a long time.

[0075] Figure 1 A flowchart of a task scheduling method based on RPA and AI provided by an embodiment of the present application is shown, and the method is applied to the RPA control center, such as Figure 1 As shown, the method may include the following steps:

[0076] Step S101: The RPA control center determines the target RPA robot group to which the concurrent authorization number is to be granted and the authorization type of the target RPA robot group.

[0077] The authorization type includes the lowest authorization type and / or the highest authorization type, that is, for the current business system, you can set a target RPA robot group with only the lowest authorization type, or a target RPA robot group with only the highest authorization type, or a target RPA robot group with both the lowest authorization type and the highest authorization type. In addition, within a business system, there can be one or more target RPA robot groups with the same authorization type.

[0078] In order to ensure that both high-frequency RPA tasks and low-frequency RPA tasks can be successfully completed on the basis of reasonably occupying RPA task execution permissions, the RPA robot group corresponding to the RPA tasks with an execution frequency less than the first preset frequency threshold can be determined as the target RPA robot group of the lowest authorization type, and / or the RPA robot group corresponding to the RPA tasks with an execution frequency greater than the second preset frequency threshold can be determined as the target RPA robot group of the highest authorization type. Among them, the first preset frequency threshold and the second preset frequency threshold can both be determined based on actual experience, and the first preset frequency threshold is less than the second preset frequency threshold.

[0079] Step S102: The RPA control center sets a target concurrent authorization number corresponding to the target RPA robot group for the target RPA robot group according to the authorization type of the target RPA robot group, so that when the authorization type of the target RPA robot group is the lowest authorization type, the target RPA robot group of the lowest authorization type is controlled to be able to concurrently execute at least the number of RPA tasks equal to the target concurrent authorization number corresponding to the target RPA robot group of the lowest authorization type; and when the authorization type of the target RPA robot group is the highest authorization type, the target RPA robot group of the highest authorization type is controlled to be able to concurrently execute at most the number of RPA tasks equal to the target concurrent authorization number corresponding to the target RPA robot group of the highest authorization type.

[0080] The target concurrent authorization number is less than the total concurrent authorization number of the business system. The target concurrent authorization numbers corresponding to different target RPA robot groups may be the same or different. The target concurrent authorization number corresponding to the target RPA robot group of the lowest authorization type is generally less than the target concurrent authorization number corresponding to the target RPA robot group of the highest authorization type. For example, the target concurrent authorization number corresponding to the target RPA robot group of the lowest authorization type may be 1, and the target concurrent authorization number corresponding to the target RPA robot group of the highest authorization type may be the total concurrent authorization number of the business system - 1.

[0081] In order to further improve the utilization rate of RPA task execution permissions, the embodiment of the present application can determine the target concurrent authorization number based on the number of RPA tasks to be executed by the target RPA robot group within an execution cycle and the total concurrent authorization number of the business system. The total concurrent authorization number of different business systems may be the same or different. For the same number of RPA tasks, the larger the total concurrent authorization number, the larger the target concurrent authorization number that can be set for the target RPA robot; for the same business system, the larger the number of RPA tasks, the larger the target concurrent authorization number that can be set for the target RPA robot. Among them, the execution cycles of the RPA tasks that different target RPA robot groups are responsible for may be the same or different. The execution cycle is the reciprocal of the above-mentioned execution frequency.

[0082] The task scheduling method based on RPA and AI provided in the embodiment of the present application can allocate the lowest target concurrent authorization number to the target RPA robot group with the lowest authorization type, so that even if most of the concurrent authorization numbers are occupied by other RPA robot groups, the target RPA robot group with the lowest authorization type can at least concurrently execute the target concurrent authorization number of RPA tasks, and / or, by allocating the highest target concurrent authorization number to the target RPA robot group with the highest authorization type, even if most of the concurrent authorization numbers are occupied by the target RPA robot group with the highest authorization type, there are still remaining concurrent authorization numbers for other RPA robot groups to use. It can be seen from this that the embodiment of the present application can avoid the problem that all RPA task execution permissions (i.e., the total concurrent authorization number of execution permissions) are occupied by some RPA robot groups, resulting in the RPA tasks that other RPA robot groups need to execute being difficult to execute for a long time, thereby improving the execution efficiency and success rate of RPA tasks in the business system as a whole.

[0083] Figure 2 A flowchart of a task scheduling method based on RPA and AI provided by another embodiment of the present application is shown, and the method is applied to the RPA control center, such as Figure 2 As shown, the method may include the following steps:

[0084] Step S201: The RPA control center determines the target RPA robot group to which the concurrent authorization number is to be granted and the authorization type of the target RPA robot group.

[0085] Step S202: The RPA control center obtains the authorized time period of the target RPA robot group.

[0086] The authorization time period is the time period during which the target RPA robot has the authority to execute PPA tasks. The authorization time period can be determined based on the effective time period during which the RPA tasks that each RPA robot group is responsible for need to be completed. When the authorization type of the target RPA robot group is the lowest authorization type, the authorization time period can be the effective time period during which the RPA tasks that the target RPA robot group of the lowest authorization type is responsible for need to be completed. When the authorization type of the target RPA robot group is the highest authorization type, the authorization time period includes the effective time period during which the RPA tasks with a lower execution frequency need to be completed, so that in addition to the concurrent authorization number owned by the target RPA robot group of the highest authorization type, there can also be a remaining concurrent authorization number for other RPA robot groups (such as RPA robot groups corresponding to RPA tasks with a lower execution rate) to use during the authorization time period.

[0087] Step S203: The RPA control center determines the authorization time for the target RPA robot group according to the start time of the authorization time period and the task running time prediction model.

[0088] The task running time prediction model is a neural network model trained based on the historical RPA task running conditions and used to predict the future running time of the RPA task. The authorization time is less than or equal to the start time of the authorization time period. The historical RPA task running conditions include the authorization waiting time for each RPA robot to obtain authorization for the responsible RPA task, the authorization success rate for each RPA robot to obtain authorization for the responsible RPA task, and the task running time of each RPA task. The task running time prediction model can be a regression model or other models. When the task running time prediction model is a regression model, the above historical RPA task running conditions can be input into the initial regression model. The regression model obtains the final required task running time prediction model after multiple training by counting and learning the dependency relationship between various parameters in the historical RPA task running conditions. The final required task running time prediction model can meet the following conditions: the difference between the future running time of the RPA task predicted by the task running time prediction model and the actual future running time of the RPA task is less than the preset error threshold.

[0089] In one embodiment, the specific implementation process of this step may be: determining the predicted time for prediction using the task runtime prediction model based on the start time of the authorization time period; when the predicted time is reached, inputting the task information of at least one running RPA task into the task runtime prediction model to obtain the future runtime of at least one running RPA task; and determining the authorization time for authorization of the target RPA robot group based on the future runtime of at least one running RPA task.

[0090] Among them, the predicted time is less than the authorized time. The predicted time can be determined based on the difference between the start time of the authorized time period and the preset time. The preset time difference can be determined based on historical experience. For example, the preset time difference is 1 hour. When the start time of the authorized time period is 12 noon, the predicted time is 11 am. Task information includes the task running start time and task completion. After obtaining the future running time of at least one running RPA task, the average future running time of at least one running RPA task can be calculated, and the time corresponding to the difference between the start time of the authorized time period and the average value is determined as the authorized time.

[0091] Step S204: When the authorization time arrives, the RPA control center sets a target concurrent authorization number for the target RPA robot group within the authorization time period based on the authorization type of the target RPA robot group, so that when the authorization type of the target RPA robot group is the lowest authorization type, the target RPA robot group of the lowest authorization type is controlled to have at least the target concurrent authorization number of tasks that can be executed concurrently within the authorization time period equal to the target concurrent authorization number corresponding to the target RPA robot group of the lowest authorization type; when the authorization type of the target RPA robot group is the highest authorization type, the target RPA robot group of the highest authorization type is controlled to have at most the target concurrent authorization number of tasks that can be executed concurrently within the authorization time period equal to the target concurrent authorization number corresponding to the target RPA robot group of the highest authorization type.

[0092] The process of an RPA robot executing an RPA task includes but is not limited to the following two processes: (1) using OCR to identify the image to be identified and obtaining the text in the image to be identified; and (2) using NLP to parse the text to be processed and obtaining the parsed text.

[0093] For example, when logging in to the client, the client may pop up a login interface containing a verification code image. In this case, the RPA robot can perform OCR recognition on the verification code image, obtain the verification code content in the verification code image, and enter the verification code content into the corresponding edit box, thereby successfully logging in to the client. When the RPA robot needs to enter the information on a page of the client into another client or other location, or needs to perform other processing on the information on a page, it can first use OCR to recognize the image contained in the page, obtain the text in the image, and then enter or otherwise process the text. Use NLP to parse the text to be processed and obtain the parsed text, including: using NLP to convert voice information into text information, using NLP to perform semantic analysis on the text to be processed, etc.

[0094] The RPA and AI-based task scheduling method provided in the embodiment of the present application can set an authorization time period for the target RPA robot group so that the target RPA robot group can have at least or at most the target authorized concurrent number of RPA task execution rights only in the authorized time period, thereby avoiding the problem that the authorized concurrent number is occupied due to the target RPA robot group not having to execute tasks outside the authorized time period, so that other RPA tasks cannot be processed in time. In addition, in order to prevent the problem that the released concurrent authorization number cannot reach the target concurrent authorization number when the target concurrent authorization number is set for the target RPA robot group at the start time of the authorized time period because other RPA tasks have not been completed, the embodiment of the present application can first determine the authorization time that is less than or equal to the start time of the authorized time period according to the start time of the authorized time period and the task running time prediction model, and set the target concurrent authorization number for the target RPA robot group when the authorized time is reached, so as to not only ensure that the concurrent authorization number that the target RPA robot group can have in the authorized time period is the target concurrent authorization number, but also make as many other RPA tasks as possible completed before the authorized time period, thereby improving the utilization rate of the RPA task execution rights.

[0095] The above scheduling method is described below with a specific example:

[0096] like Figure 3 As shown, the RPA platform includes an RPA control center, RPA robot group A and RPA robot group B. RPA robot group A includes m RPA robots (RPA robot m1 to RPA robot mm), and RPA robot group B includes n RPA robots (RPA robot n1 to RPA robot nn). The RPA tasks that RPA robot group A is responsible for have a high execution frequency and a large RPA task volume. The RPA tasks that RPA robot group B is responsible for have a low execution frequency and a small RPA task volume, but the RPA tasks are very important. RPA robot group A and RPA robot group B are responsible for serving the business system X, and the total concurrent authorization number allocated to business system X is p.

[0097] To prevent RPA robot group A from occupying all the permissions to execute RPA tasks, which causes RPA robot group B to be unable to process tasks in a timely manner, the following scheduling methods can be used, but are not limited to:

[0098] (I) Assuming that the RPA task that robot group B is responsible for must be completed between 9 and 10 a.m., the RPA control center can set the minimum concurrent authorization number for robot group B during the period from 9 to 10 a.m. before 9 a.m. For example, the minimum concurrent authorization number is 1, so that robot group B can concurrently execute at least one RPA task during the period from 9 to 10 a.m., and will not be unable to execute RPA tasks during this period.

[0099] (II) Assuming that the RPA tasks that robot group B is responsible for must be completed between 9 am and 10 am, and the RPA tasks that robot group A is responsible for must be completed between 8 am and 5 pm, then the RPA control center can set the maximum concurrent authorization number for robot group A in advance before 8 am during the period from 8 am to 5 pm. For example, the maximum concurrent authorization number is p-1, so that robot group A can concurrently execute at most p-1 RPA tasks during the period from 8 am to 5 pm, and robot group B can obtain 1 concurrent authorization number from 8 am to 10 am, so that it can execute RPA tasks during this period, and there will be no situation where RPA tasks cannot be executed during this period.

[0100] Figure 4 Another embodiment of the present application provides a task scheduling device based on RPA and AI. The method is applied to the RPA control center. The device includes:

[0101] A determination unit 310 is used to determine a target RPA robot group to be granted a concurrent authorization number and an authorization type of the target RPA robot group, wherein the authorization type includes a minimum authorization type and / or a maximum authorization type;

[0102] The setting unit 320 is used to set a target concurrent authorization number corresponding to the target RPA robot group for the target RPA robot group according to the authorization type of the target RPA robot group, so that when the authorization type of the target RPA robot group is the lowest authorization type, the target RPA robot group of the lowest authorization type is controlled to be able to concurrently execute at least the number of RPA tasks equal to the target concurrent authorization number corresponding to the target RPA robot group of the lowest authorization type, and when the authorization type of the target RPA robot group is the highest authorization type, the target RPA robot group of the highest authorization type is controlled to be able to concurrently execute at most the number of RPA tasks equal to the target concurrent authorization number corresponding to the target RPA robot group of the highest authorization type, wherein the target concurrent authorization number is less than the total concurrent authorization number of the business system.

[0103] In one implementation, the setting unit 320 includes:

[0104] An acquisition module is used to acquire the authorized time period of the target RPA robot group, wherein the authorized time period is the time period during which the target RPA robot has the authority to execute the RPA task;

[0105] An authorization time determination module is used to determine the authorization time for the target RPA robot group to be authorized based on the start time of the authorization time period and the task running time prediction model, wherein the task running time prediction model is a neural network model trained based on the historical RPA task running conditions and used to predict the future running time of the RPA task, and the authorization time is less than or equal to the start time of the authorization time period;

[0106] A setting module is used to set a target concurrent authorization number for the target RPA robot group within the authorization time period according to the authorization type of the target RPA robot group when the authorization time arrives, so that when the authorization type of the target RPA robot group is the lowest authorization type, the target RPA robot group of the lowest authorization type is controlled to have at least the target concurrent authorization number of tasks that can be executed concurrently within the authorization time period, and when the authorization type of the target RPA robot group is the highest authorization type, the target RPA robot group of the highest authorization type is controlled to have at most the target concurrent authorization number of tasks that can be executed concurrently within the authorization time period, and the target RPA robot group of the highest authorization type is controlled to have the target concurrent authorization number of tasks that can be executed concurrently within the authorization time period.

[0107] In one embodiment, the authorization time determination module includes:

[0108] A first determination submodule is used to determine a prediction time for prediction using a task runtime prediction model according to a start time of the authorization time period, wherein the prediction time is less than the authorization time;

[0109] A prediction submodule, configured to input task information of at least one running RPA task into a task running time prediction model when a prediction time is reached, and obtain a future running time of at least one running RPA task, wherein the task information includes a task running start time and a task completion degree;

[0110] The second determination submodule is used to determine the authorization time for authorizing the target RPA robot group according to the future running time of at least one running RPA task.

[0111] In one embodiment, the second determination submodule is used to calculate the average future running time of at least one running RPA task, and determine the time corresponding to the difference between the start time of the authorized time period and the average value as the authorized time.

[0112] In one embodiment, the historical RPA task running status includes the authorization waiting time for each RPA robot to obtain authorization for the responsible RPA task, the authorization success rate for each RPA robot to obtain authorization for the responsible RPA task, and the task running time of each RPA task.

[0113] In one implementation, the determination unit 310 includes a first group determination module and / or a second group determination module;

[0114] A first group determination module is used to group the RPA robots corresponding to the RPA tasks whose execution frequency is less than a first preset frequency threshold, and determine them as the target RPA robot group of the lowest authorization type;

[0115] A second group determination module is used to group the RPA robots corresponding to the RPA tasks whose execution frequency is greater than the second preset frequency threshold, and determine them as the target RPA robot group of the highest authorization type;

[0116] The first preset frequency threshold is smaller than the second preset frequency threshold.

[0117] In one implementation, the determination unit 310 is further configured to determine a target concurrent authorization number based on the number of RPA tasks to be executed by the target RPA robot group within an execution cycle and the total concurrent authorization number of the business system.

[0118] The functions of each module in each device in the embodiments of the present application can be found in the corresponding description in the above method, and will not be repeated here.

[0119] Figure 5 The structural block diagram of an electronic device provided by another embodiment of the present application is shown. The electronic device includes: a processor 410 and a memory 420, the memory 420 stores instructions, and the instructions are loaded and executed by the processor 410 to implement a method as in any of the above method embodiments. The number of the memory 420 and the processor 410 can be one or more.

[0120] like Figure 6 As shown, the electronic device also includes:

[0121] The communication interface 430 is used to communicate with external devices and perform data exchange transmission.

[0122] If the memory 420, the processor 410 and the communication interface 430 are implemented independently, the memory 420, the processor 410 and the communication interface 430 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0123] In a specific implementation, if the memory 420, the processor 410 and the communication interface 430 are integrated on a chip, the memory 420, the processor 410 and the communication interface 430 can communicate with each other through an internal interface.

[0124] An embodiment of the present application provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, a method provided in any of the above method embodiments is implemented.

[0125] An embodiment of the present application also provides a chip, which includes a processor for calling and executing instructions stored in the memory from the memory, so that a communication device equipped with the chip executes the method provided by the embodiment of the present application.

[0126] An embodiment of the present application also provides a chip, including: an input interface, an output interface, a processor and a memory, wherein the input interface, the output interface, the processor and the memory are connected via an internal connection path, and the processor is used to execute the code in the memory. When the code is executed, the processor is used to execute the method provided in the embodiment of the application.

[0127] It should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. It is worth noting that the processor may be a processor supporting the advanced RISC machines (ARM) architecture.

[0128] Further, optionally, the above-mentioned memory may include a read-only memory and a random access memory, and may also include a non-volatile random access memory. The memory may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may include a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may include a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available. For example, static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM) and direct rambus RAM (DR RAM).

[0129] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function according to the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium.

[0130] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples, unless they are contradictory.

[0131] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0132] Any process or method description in the flow chart or otherwise described herein can be understood to represent a module, fragment or portion of a code including one or more executable instructions for implementing the steps of a specific logical function or process. And the scope of the preferred embodiment of the present application includes other implementations, in which the functions may not be performed in the order shown or discussed, including in a substantially simultaneous manner or in a reverse order according to the functions involved.

[0133] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, which can be embodied in any computer-readable medium for use by an instruction execution system, apparatus or device (such as a computer-based system, a system including a processor or other system that can fetch instructions from an instruction execution system, apparatus or device and execute instructions), or used in combination with these instruction execution systems, apparatuses or devices.

[0134] It should be understood that the various parts of the present application can be implemented with hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented with software or firmware stored in a memory and executed by a suitable instruction execution system. All or part of the steps of the above embodiment method can be completed by instructing the relevant hardware through a program, which can be stored in a computer-readable storage medium, and when the program is executed, it includes one of the steps of the method embodiment or a combination thereof.

[0135] In addition, each functional unit in each embodiment of the present application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into one module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of a software functional module. If the above-mentioned integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. The storage medium can be a read-only memory, a disk or an optical disk, etc.

[0136] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A task scheduling method based on RPA and AI, the method is applied to the RPA control center, characterized in that: The method comprises: S1. Determine the target Robotic Process Automation (RPA) robot group and the authorization type of the target RPA robot group to be granted the number of concurrent authorizations, wherein the authorization type includes a minimum authorization type and / or a maximum authorization type; S2. According to the authorization type of the target RPA robot group, a target concurrent authorization number corresponding to the target RPA robot group is set for the target RPA robot group, so that when the authorization type of the target RPA robot group is the lowest authorization type, the number of RPA tasks that can be executed concurrently by the target RPA robot group of the lowest authorization type is controlled to be at least the target concurrent authorization number corresponding to the target RPA robot group of the lowest authorization type; when the authorization type of the target RPA robot group is the highest authorization type, the number of RPA tasks that can be executed concurrently by the target RPA robot group of the highest authorization type is controlled to be the target concurrent authorization number corresponding to the target RPA robot group of the highest authorization type, wherein the target concurrent authorization number is less than the total concurrent authorization number of the business system; In S2, according to the authorization type of the target RPA robot group, setting a target concurrent authorization number corresponding to the target RPA robot group for the target RPA robot group includes: S21. Obtain an authorized time period of the target RPA robot group, wherein the authorized time period is a time period during which the target RPA robot has the authority to execute PRA tasks; S22. Determine the authorization time for the target RPA robot group to be authorized according to the start time of the authorization time period and the task running time prediction model, wherein the task running time prediction model is a neural network model trained according to the historical RPA task running conditions and used to predict the future running time of the RPA task, and the authorization time is less than or equal to the start time of the authorization time period; S23. When the authorization time arrives, according to the authorization type of the target RPA robot group, set the target concurrent authorization number within the authorization time period for the target RPA robot group.

2. The method according to claim 1, characterized in that S22 includes: S221, determining a prediction time for prediction using the task runtime prediction model according to the start time of the authorization time period, wherein the prediction time is less than the authorization time; S222: When the predicted time is reached, input task information of at least one running RPA task into the task running time prediction model to obtain the future running time of the at least one running RPA task, wherein the task information includes the task running start time and the task completion degree; S223. Determine an authorization time for authorizing the target RPA robot group according to the future running time of the at least one running RPA task.

3. The method according to claim 2, characterized in that The S223 includes: S2231. Calculate the average future running time of the at least one running RPA task; S2232: Determine a time corresponding to a difference between a start time of the authorization time period and the average value as the authorization time.

4. The method according to claim 1, characterized in that The historical RPA task running status includes the authorization waiting time for each RPA robot to obtain authorization for the responsible RPA task, the authorization success rate for each RPA robot to obtain authorization for the responsible RPA task, and the task running time of each RPA task.

5. The method according to claim 1, characterized in that S1 includes: S11. Group the RPA robots corresponding to the RPA tasks whose execution frequency is less than the first preset frequency threshold, and determine them as the target RPA robot group of the lowest authorized type; And / or, S12, grouping the RPA robots corresponding to the RPA tasks having an execution frequency greater than a second preset frequency threshold, and determining them as the target RPA robot group of the highest authorized type; The first preset frequency threshold is smaller than the second preset frequency threshold.

6. The method according to any one of claims 1 to 5, characterized in that Before S2, the method further includes: S3. Determine the target concurrent authorization number according to the number of RPA tasks to be executed by the target RPA robot group within one execution cycle and the total concurrent authorization number of the business system.

7. A task scheduling device based on RPA and AI, the device is applied to the RPA control center, characterized in that: The device comprises: A determination unit, configured to determine the target Robotic Process Automation (RPA) robot group and the authorization type of the target RPA robot group to which the concurrent authorization number is to be granted, wherein the authorization type includes a minimum authorization type and / or a maximum authorization type; a setting unit, configured to set a target concurrent authorization number corresponding to the target RPA robot group for the target RPA robot group according to the authorization type of the target RPA robot group, so that when the authorization type of the target RPA robot group is the lowest authorization type, the number of RPA tasks that can be executed concurrently by the target RPA robot group of the lowest authorization type is controlled to be at least the target concurrent authorization number corresponding to the target RPA robot group of the lowest authorization type; and when the authorization type of the target RPA robot group is the highest authorization type, the number of RPA tasks that can be executed concurrently by the target RPA robot group of the highest authorization type is controlled to be the target concurrent authorization number corresponding to the target RPA robot group of the highest authorization type, wherein the target concurrent authorization number is less than the total concurrent authorization number of the business system; The setting unit comprises: An acquisition module, configured to acquire an authorized time period of the target RPA robot group, wherein the authorized time period is a time period during which the target RPA robot has the authority to execute the RPA task; An authorization time determination module, used to determine the authorization time for the target RPA robot group to be authorized according to the start time of the authorization time period and the task running time prediction model, wherein the task running time prediction model is a neural network model trained according to the historical RPA task running conditions and used to predict the future running time of the RPA task, and the authorization time is less than or equal to the start time of the authorization time period; A setting module is used to set the target concurrent authorization number within the authorization time period for the target RPA robot group according to the authorization type of the target RPA robot group when the authorization time arrives.

8. The device according to claim 7, characterized in that The authorization time determination module includes: A first determination submodule is used to determine a prediction time for prediction using the task runtime prediction model according to the start time of the authorization time period, wherein the prediction time is less than the authorization time; a prediction submodule, configured to input task information of at least one running RPA task into the task running time prediction model when the predicted time is reached, so as to obtain the future running time of the at least one running RPA task, wherein the task information includes the task running start time and the task completion degree; The second determination submodule is used to determine the authorization time for authorizing the target RPA robot group according to the future running time of the at least one running RPA task.

9. The device according to claim 8, characterized in that The second determination submodule is used to calculate the average future running time of the at least one running RPA task, and determine the time corresponding to the difference between the start time of the authorized time period and the average value as the authorized time.

10. The device according to claim 7, characterized in that The determination unit includes a first group determination module and / or a second group determination module; The first group determination module is used to group the RPA robots corresponding to the RPA tasks whose execution frequency is less than the first preset frequency threshold, and determine them as the target RPA robot group of the minimum authorized type; The second group determination module is used to determine the RPA robot group corresponding to the RPA tasks whose execution frequency is greater than the second preset frequency threshold as the target RPA robot group of the highest authorization type; The first preset frequency threshold is smaller than the second preset frequency threshold.

11. The device according to any one of claims 7 to 10, characterized in that The determination unit is further used to determine the target concurrent authorization number according to the number of RPA tasks to be executed by the target RPA robot group within an execution cycle and the total concurrent authorization number of the business system.

12. An electronic device, characterized in that: The electronic device comprises a processor and a memory, wherein the memory stores instructions, and the instructions are loaded and executed by the processor to implement the method according to any one of claims 1 to 6.

13. A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

Images