RPA robot control method and device, computer device and storage medium

Abstract

The embodiment of the application belongs to the technical field of RPA robots, and relates to an RPA robot control method, which comprises the following steps: receiving a to-be-processed task; acquiring performance evaluation information of each RPA robot in an RPA cluster; selecting at least one RPA robot from the RPA cluster according to the performance evaluation information of the RPA robots; generating execution configuration information based on the at least one selected RPA robot and the to-be-processed task; and calling the at least one RPA robot to execute the to-be-processed task according to the execution configuration information, so as to obtain a task execution result. The application also provides an RPA robot control device, a computer device and a storage medium. The application realizes accurate management and control of RPA robots in an RPA cluster.

Description

Technical Field

[0001] This application relates to the field of RPA robot technology, and in particular to an RPA robot control method, device, computer equipment and storage medium. Background Technology

[0002] Robotic process automation (RPA), also known as software robot, refers to the use of intelligent automation technology to perform repetitive tasks that human workers perform. RPA robots require pre-designed usage rules, followed by the deployment of scripts that simulate human processes, allowing the RPA robot to autonomously execute various activities and tasks.

[0003] RPA robots are now widely used, and in applications, multiple RPA robots are often used to form an RPA cluster. However, current RPA robot technology manages individual RPA robots within the cluster, issuing simple commands such as start and stop, but it cannot provide more effective management and control of RPA robots. Summary of the Invention

[0004] The purpose of this application is to provide an RPA robot control method, device, computer equipment, and storage medium to solve the problem of accurate management and control of RPA robots in an RPA cluster.

[0005] To address the aforementioned technical problems, this application provides an RPA robot control method, which employs the following technical solution:

[0006] Receive pending tasks;

[0007] Obtain performance evaluation information for each RPA robot in the RPA cluster;

[0008] Based on the performance evaluation information of each RPA robot, at least one RPA robot is selected from the RPA cluster;

[0009] Execution configuration information is generated based on at least one selected RPA robot and the task to be processed;

[0010] Based on the execution configuration information, at least one RPA robot is invoked to execute the task to be processed, and the task execution result is obtained.

[0011] Furthermore, the step of obtaining performance evaluation information for each RPA robot in the RPA cluster includes:

[0012] Obtain the first performance evaluation factor and the second performance evaluation factor of each RPA robot in the RPA cluster. The first performance evaluation factor is associated with the first environment in which the RPA robot is located, and the second performance evaluation factor is associated with the second environment in which the RPA robot is located.

[0013] Based on the first performance evaluation factor and the second performance evaluation factor of each RPA robot, the performance evaluation information of each RPA robot is generated respectively.

[0014] Furthermore, the step of selecting at least one RPA robot from the RPA cluster based on the performance evaluation information of each RPA robot includes:

[0015] Calculate the performance evaluation value of each RPA robot based on the performance evaluation information of each RPA robot;

[0016] At least one RPA robot is selected from the RPA robots based on the obtained performance evaluation values.

[0017] Furthermore, the step of generating execution configuration information based on at least one selected RPA robot and the task to be processed includes:

[0018] Obtain the task evaluation information of the task to be processed;

[0019] Based on at least one selected RPA robot and the task evaluation information, execution configuration information is generated, which includes the RPA working mode and execution order type.

[0020] Furthermore, the step of invoking at least one RPA robot to execute the task to be processed according to the execution configuration information and obtaining the task execution result includes:

[0021] The task execution queue of the at least one RPA robot is adjusted according to the execution order type in the execution configuration information, wherein the execution order type includes sequential execution and priority execution;

[0022] Based on the adjusted task execution queue, at least one RPA robot is invoked according to the RPA working mode in the execution configuration information to execute the task to be processed through the at least one RPA robot and obtain the task execution result.

[0023] Furthermore, the method also includes:

[0024] For each RPA robot, obtain the communication status of the RPA robot;

[0025] When the RPA robot is in the first communication state, it communicates with the RPA robot according to a preset first communication method, wherein the first communication method is network communication;

[0026] When the RPA robot is in the second communication state, it communicates with the RPA robot according to a preset second communication method, which includes Bluetooth, infrared and LoRa.

[0027] Furthermore, the method also includes:

[0028] Acquire the data to be processed, which includes the running data and running logs of each RPA robot, as well as the task execution results of each task to be processed;

[0029] The data to be processed is cleaned to obtain cleaned data;

[0030] The cleaned data is processed according to a preset data processing strategy to obtain processed data, wherein the data processing strategy is matched with the data type of the data to be processed.

[0031] To address the aforementioned technical problems, this application also provides an RPA robot control device, which employs the following technical solution:

[0032] The task receiving module is used to receive tasks to be processed.

[0033] The evaluation acquisition module is used to acquire performance evaluation information of each RPA robot in the RPA cluster;

[0034] The selection module is used to select at least one RPA robot from the RPA cluster based on the performance evaluation information of each RPA robot;

[0035] A configuration generation module is used to generate execution configuration information based on at least one selected RPA robot and the task to be processed;

[0036] The task execution module is used to invoke at least one RPA robot to execute the task to be processed according to the execution configuration information, and obtain the task execution result.

[0037] To address the aforementioned technical problems, this application also provides a computer device, which includes a memory and a processor. The memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of the RPA robot control method described above.

[0038] To address the aforementioned technical problems, this application also provides a computer-readable storage medium storing computer-readable instructions, which, when executed by a processor, implement the steps of the RPA robot control method described above.

[0039] Compared with the prior art, the embodiments of this application have the following main advantages: receiving tasks to be processed; obtaining performance evaluation information of each RPA robot in the RPA cluster, the performance evaluation information is used to evaluate the task processing capability of each RPA robot, so that at least one RPA robot with strong task processing capability can be selected based on the performance evaluation information, ensuring the processing efficiency of the tasks to be processed; generating execution configuration information based on the selected at least one RPA robot and the tasks to be processed, the execution configuration information instructs the selected at least one RPA robot how to process the tasks to be processed, further ensuring that the tasks to be processed can be executed reasonably, improving the efficiency and accuracy of the task execution results obtained after task execution, and realizing precise management and control of RPA robots in the RPA cluster. Attached Figure Description

[0040] To more clearly illustrate the solutions in this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is an exemplary system architecture diagram to which this application can be applied;

[0042] Figure 2 This is a flowchart of one embodiment of the RPA robot control method according to this application;

[0043] Figure 3 This is a schematic diagram of one embodiment of the RPA robot control device according to this application;

[0044] Figure 4 This is a schematic diagram of the structure of one embodiment of the computer device according to this application. Detailed Implementation

[0045] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein in the specification of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having," and any variations thereof, in the specification, claims, and foregoing drawings of this application, are intended to cover non-exclusive inclusion. The terms "first," "second," etc., in the specification, claims, or foregoing drawings of this application are used to distinguish different objects, not to describe a particular order.

[0046] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0047] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0048] like Figure 1 As shown, system architecture 100 may include terminal devices 101, 102, and 103, a network 104, and a server 105. Network 104 serves as the medium for providing communication links between terminal devices 101, 102, and 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0049] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social media platform software, etc.

[0050] Terminal devices 101, 102, and 103 can be various electronic devices with displays and web browsing capabilities, including but not limited to smartphones, tablets, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III), MP4 players (Moving Picture Experts Group Audio Layer IV), laptops, and desktop computers, etc. Each terminal device runs at least one RPA robot, and the RPA robots on all terminal devices constitute an RPA cluster.

[0051] Server 105 can be a server that provides various services, such as a backend server that supports the pages displayed on terminal devices 101, 102, and 103.

[0052] It should be noted that the RPA robot control method provided in this application embodiment is generally executed by a server, and correspondingly, the RPA robot control device is generally set in the server.

[0053] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0054] Continue to refer to Figure 2 A flowchart of an embodiment of the RPA robot control method according to this application is shown. The RPA robot control method includes the following steps:

[0055] Step S201: Receive the task to be processed.

[0056] In this embodiment, the RPA robot control method operates on electronic devices (e.g., Figure 1 The server shown can communicate with each terminal via wired or wireless connections. It should be noted that the aforementioned wireless connection methods may include, but are not limited to, 3G / 4G / 5G connections, WiFi connections, Bluetooth connections, WiMAX connections, Zigbee connections, UWB (ultra wideband) connections, and other currently known or future wireless connection methods.

[0057] Specifically, it receives pending tasks, which are tasks to be processed by RPA robots in the RPA cluster.

[0058] Step S202: Obtain performance evaluation information for each RPA robot in the RPA cluster.

[0059] Specifically, the performance evaluation information of each RPA robot in the RPA cluster is obtained. The performance evaluation information can include multiple evaluation factors to evaluate the task processing performance of each RPA robot.

[0060] In this application, an RPA robot can run on multiple terminal devices, and multiple RPA robots can run on a single terminal device. The status of the RPA robot includes idle, working, and offline. The performance evaluation information here can refer to the performance evaluation information of the RPA robot already running on the terminal device; or it can refer to the performance evaluation information of the new RPA robot after it has been assigned and run on a terminal device.

[0061] Step S203: Select at least one RPA robot from the RPA cluster based on the performance evaluation information of each RPA robot.

[0062] Specifically, based on the performance evaluation information of each RPA robot in the RPA cluster, the task processing performance / processing capability of each RPA robot can be determined. Based on the performance evaluation information of each RPA robot, at least one RPA robot can be selected from all RPA robots. It can be understood that the selected RPA robot should have strong task processing capabilities.

[0063] Furthermore, the selected RPA robot can be an RPA robot already running on the terminal device, or it can be a new RPA robot that will be assigned and run on a terminal device after being selected.

[0064] Step S204: Generate execution configuration information based on the selected RPA robot and the task to be processed.

[0065] Specifically, based on at least one selected RPA robot and the task to be processed, execution configuration information is generated. The execution configuration information is equivalent to an instruction that instructs the RPA robot to process the task to be processed. For example, the execution configuration information may indicate when to execute the task to be processed.

[0066] When more than two RPA robots are selected, each RPA robot needs to work together to process the task. In this case, it is also necessary to set the relationship and connection between each RPA robot during the task execution process. For example, the task to be processed can be divided into multiple sub-tasks, and different sub-tasks can be assigned to different RPA robots for processing, with a certain RPA robot generating the final result. The above information can be recorded in the execution configuration information.

[0067] Step S205: Invoke at least one RPA robot to execute the task to be processed according to the execution configuration information, and obtain the task execution result.

[0068] Specifically, based on the generated execution configuration information, at least one RPA robot is invoked to execute the task to be processed, and the task execution result is generated.

[0069] In this embodiment, a task to be processed is received; performance evaluation information of each RPA robot in the RPA cluster is obtained. The performance evaluation information is used to evaluate the task processing capability of each RPA robot, so that at least one RPA robot with strong task processing capability can be selected based on the performance evaluation information, ensuring the processing efficiency of the task to be processed; execution configuration information is generated based on the selected at least one RPA robot and the task to be processed. The execution configuration information instructs the selected at least one RPA robot on how to process the task to be processed, further ensuring that the task to be processed can be executed reasonably, improving the efficiency and accuracy of the task execution results obtained after task execution, and realizing precise management and control of the RPA robots in the RPA cluster.

[0070] Furthermore, step S202 may include: obtaining a first performance evaluation factor and a second performance evaluation factor for each RPA robot in the RPA cluster, wherein the first performance evaluation factor is associated with a first environment in which the RPA robot is located, and the second performance evaluation factor is associated with a second environment in which the RPA robot is located; and generating performance evaluation information for each RPA robot based on the first performance evaluation factor and the second performance evaluation factor for each RPA robot.

[0071] Specifically, each RPA robot in the RPA cluster has a runtime environment. In this application, the runtime environment of the RPA robot is divided into a first environment and a second environment. The first environment may refer to the virtual machine where the RPA robot resides; the second environment may be the terminal device where the RPA robot resides, or the terminal device where the virtual machine resides.

[0072] The RPA robot has a first performance evaluation factor and a second performance evaluation factor, both of which can be multi-dimensional evaluation factors. The first performance evaluation factor is associated with the first environment and reflects relevant parameters of the first environment, such as: whether there are pending tasks, the number of pending tasks historically, execution time and frequency, and virtual machine CPU usage. The second performance evaluation factor is associated with the second environment and reflects relevant parameters of the second environment, such as: whether the terminal device is idle, whether it is currently processing tasks, and CPU usage. It can be understood that the characteristics of the first and second performance evaluation factors can be similar, except that one represents the first environment and the other the second environment.

[0073] The first performance evaluation factor and the second performance evaluation factor of each RPA robot constitute the performance evaluation information of each RPA robot.

[0074] When the RPA robot runs directly in the first environment, it can obtain only its first evaluation factor (with the second evaluation factor marked as empty), or it can obtain both the first performance evaluation factor and the second performance evaluation factor (with the second evaluation factor not being empty). When the RPA robot runs in the second environment, it can obtain only its second evaluation factor (with the first evaluation factor marked as empty), or it can obtain both the first performance evaluation factor (with the first evaluation factor marked as empty) and the second performance evaluation factor.

[0075] In this embodiment, a first performance evaluation factor and a second performance evaluation factor are obtained for each RPA robot. The first performance evaluation factor is associated with the first environment in which the RPA robot is located, and the second performance evaluation factor is associated with the second environment in which the RPA robot is located. The first performance evaluation factor and the second performance evaluation factor of each RPA robot constitute the performance evaluation information of each RPA robot, ensuring that a comprehensive performance evaluation of the RPA robot can be performed.

[0076] Furthermore, step S203 may include: calculating the performance evaluation value of each RPA robot based on the performance evaluation information of each RPA robot; and selecting at least one RPA robot from among the RPA robots based on the obtained performance evaluation value.

[0077] Specifically, for each RPA robot, factor weights can be added to each specific evaluation factor in its performance evaluation information. These factor weights can be pre-set or calculated using weighting algorithms such as the CRITIC algorithm or the analytic hierarchy process.

[0078] The performance evaluation value of the RPA robot is obtained by weighting the evaluation factors and their corresponding weights. The value represents the task processing capability of the RPA robot.

[0079] Each RPA robot can calculate a performance evaluation value, and select at least one RPA robot from among all RPA robots based on the performance evaluation value, for example, selecting one or several RPA robots with the highest performance evaluation value.

[0080] In one embodiment, various evaluation factors in the performance evaluation information can also be input into the performance evaluation model. The performance evaluation model can be built based on the LGBM model and pre-trained. The performance evaluation model classifies the RPA robot according to each evaluation factor. Each category has a preset performance evaluation value, and the performance evaluation value of the category is used as the performance evaluation value of the RPA robot.

[0081] In this embodiment, based on the performance evaluation information of each RPA robot, the performance evaluation value of each RPA robot is calculated. The performance evaluation value represents the task processing capability of each RPA robot in terms of numerical value. Based on the performance evaluation value, at least one RPA robot with strong task processing capability is selected from all RPA robots to ensure the execution efficiency of the task to be processed.

[0082] Furthermore, step S204 may include: obtaining task evaluation information of the task to be processed; generating execution configuration information based on the selected at least one RPA robot and the task evaluation information, wherein the execution configuration information includes the RPA working mode and execution order type.

[0083] Specifically, the task to be processed has task evaluation information, which can be multi-dimensional and used to evaluate and describe the task. For example, it includes the amount of data in the data table that the task needs to process, the urgency of the task, and the execution conditions of the task.

[0084] Based on at least one selected RPA robot and task evaluation information, execution configuration information is generated, including the RPA working mode and execution order type. Since multiple RPA robots can be selected, they have cooperative or related relationships in data processing. This relationship can be defined through RPA working modes, including master-master, master-slave, slave-slave, and single-robot modes. Master-master means all RPA robots provide services simultaneously, achieving load balancing; master-slave means one RPA robot acts as the master, and the others act as slaves, with "slave" meaning a servant assisting the master in data processing; slave-slave means all RPA robots are slaves; and single-robot mode refers to the case where only one RPA robot is selected.

[0085] The RPA working mode can be preset, set randomly, or determined based on the amount of data to be processed recorded in the task evaluation information. For example, when the amount of data is small, an RPA robot can be selected and the single-unit mode can be used; when the amount of data is large, the master-slave mode can be selected; and when the amount of data is even larger, the master-master mode can be selected.

[0086] The execution order can be determined based on the urgency of the task and the execution conditions. For example, urgent tasks can be executed first, while scheduled tasks can be executed normally.

[0087] In this embodiment, task evaluation information of the task to be processed is obtained, which evaluates and describes the task to be processed; based on the selected RPA robot and the task evaluation information, execution configuration information is generated, which includes the RPA working mode and execution order type, and instructs the execution of the task.

[0088] Furthermore, step S205 may include: adjusting the task execution queue of at least one RPA robot according to the execution order type in the execution configuration information, wherein the execution order type includes sequential execution and priority execution; and invoking at least one RPA robot based on the RPA working mode in the execution configuration information according to the adjusted task execution queue, so as to execute the task to be processed through at least one RPA robot and obtain the task execution result.

[0089] Specifically, the RPA robot has a task execution queue, which records the pending tasks that the RPA robot will execute.

[0090] The execution order type in the configuration information includes sequential execution and priority execution. Sequential execution executes tasks in the normal order, adding them to the tail of the task execution queue, and the RPA robot executes them from front to back. Priority execution means that tasks with higher priority are added to the head of the task execution queue, and the RPA robot executes them from front to back. When adding tasks to the head of the task execution queue, they can be added to the very front, or the urgency of the task can be compared with the urgency of other tasks at the head, and the tasks at the head can be reordered according to their urgency.

[0091] It is understandable that if the task to be processed involves multiple RPA robots, the task execution queue of each RPA robot needs to undergo the same processing.

[0092] Based on the adjusted task execution queue, at least one RPA robot is invoked according to the RPA working mode in the execution configuration information, and the task to be processed is executed through at least one RPA robot to obtain the task execution result.

[0093] In this embodiment, the task execution queue of the RPA robot is adjusted according to the execution order type in the execution configuration information. Based on the adjusted task execution queue and the execution configuration information, the RPA robot is invoked to execute the tasks to be processed, thus ensuring the correct processing of the tasks to be processed.

[0094] Furthermore, the above-mentioned RPA robot control method may also include: for each RPA robot, acquiring the communication status of the RPA robot; when the RPA robot is in a first communication state, communicating with the RPA robot according to a preset first communication method, the first communication method being network communication; when the RPA robot is in a second communication state, communicating with the RPA robot according to a preset second communication method, the second communication method including Bluetooth, infrared, and LoRa.

[0095] Specifically, RPA robots have a communication state, which refers to the communication method between the RPA robot and the server. It can be understood that it is also the communication method between the terminal device and the server.

[0096] The communication state has a first communication state and a second communication state. The first communication state means that the RPA robot and the server can communicate through the network. In this case, the server communicates with the RPA robot according to the first communication method, which is network communication, including wired network communication and wireless network communication.

[0097] When the RPA robot is in the second communication state, the RPA robot and the server cannot communicate via the network. This application improves the RPA robot and the server so that, in the second communication state, communication with the RPA robot can be achieved through a second communication method, including Bluetooth, infrared, and LoRa (a low-power local area network wireless standard, also known as long-range radio).

[0098] In this embodiment, the communication status of the RPA robot is obtained, and different communication methods are used to communicate according to the communication status, ensuring that communication with the RPA robot can be carried out in various situations, and that the RPA robot can be controlled in various situations.

[0099] Furthermore, the above-mentioned RPA robot control method may also include: acquiring data to be processed, including the running data and running logs of each RPA robot, and the task execution results of each task to be processed; cleaning the data to be processed to obtain cleaned data; and processing the cleaned data according to a preset data processing strategy to obtain processed data, wherein the data processing strategy is matched with the data type of the data to be processed.

[0100] Specifically, the data to be processed is acquired. The data to be processed includes the running data and running logs of each RPA robot, as well as the task execution results of each task to be processed. Among them, the running data can be the data generated by the RPA robot during operation, and the running log refers to the logs generated by the RPA robot during operation. Regardless of whether the RPA robot has executed the task to be processed, as long as it is running on the terminal device, running data and running log will be generated.

[0101] The server performs data cleaning on the data to be processed, obtaining cleaned data. Based on the data type, it obtains the corresponding data processing strategy; for example, different data processing strategies are needed for runtime logs and task execution results, as task execution results may contain more business elements. The cleaned data is then processed according to the preset data processing strategy to obtain processed data, completing the data fusion and aggregation process in the RPA cluster.

[0102] In this embodiment, the data to be processed is acquired, including the running data and logs of each RPA robot, as well as the task execution results of each task to be processed; the data to be processed is cleaned to obtain cleaned data; the cleaned data is processed according to a preset data processing strategy to obtain processed data, thus completing the data fusion and aggregation processing in the RPA cluster.

[0103] The server in this application may include a cluster center, a control component, a monitoring component, and a data processing platform. The cluster center is used for comprehensive scheduling and analysis, such as selecting RPA robots and generating execution configuration information; the control component is used to implement specific scheduling and task execution of the RPA robot cluster; the resource monitoring component is used to collect performance evaluation information, operating data, and operating logs of each RPA robot; and the data processing platform acquires and processes the data to be processed.

[0104] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by instructing related hardware with computer-readable instructions. These computer-readable instructions can be stored in a computer-readable storage medium. When executed, the program can include the processes of the embodiments of the above methods. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, optical disk, or read-only memory (ROM), or random access memory (RAM).

[0105] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0106] Further reference Figure 3 As a response to the above Figure 2 The implementation of the method shown in this application provides an embodiment of an RPA robot control device, which is similar to... Figure 2 Corresponding to the method embodiments shown, this device can be specifically applied to various electronic devices.

[0107] like Figure 3 As shown, the RPA robot control device 300 described in this embodiment includes: a task receiving module 301, an evaluation acquisition module 302, a selection module 303, a configuration generation module 304, and a task execution module 305, wherein:

[0108] The task receiving module 301 is used to receive tasks to be processed.

[0109] The evaluation acquisition module 302 is used to acquire performance evaluation information of each RPA robot in the RPA cluster.

[0110] The selection module 303 is used to select at least one RPA robot from the RPA cluster based on the performance evaluation information of each RPA robot.

[0111] The configuration generation module 304 is used to generate execution configuration information based on at least one selected RPA robot and the task to be processed.

[0112] The task execution module 305 is used to call at least one RPA robot to execute the task to be processed according to the execution configuration information and obtain the task execution result.

[0113] In this embodiment, a task to be processed is received; performance evaluation information of each RPA robot in the RPA cluster is obtained. The performance evaluation information is used to evaluate the task processing capability of each RPA robot, so that at least one RPA robot with strong task processing capability can be selected based on the performance evaluation information, ensuring the processing efficiency of the task to be processed; execution configuration information is generated based on the selected at least one RPA robot and the task to be processed. The execution configuration information instructs the selected at least one RPA robot on how to process the task to be processed, further ensuring that the task to be processed can be executed reasonably, improving the efficiency and accuracy of the task execution results obtained after task execution, and realizing precise management and control of the RPA robots in the RPA cluster.

[0114] In some optional implementations of this embodiment, the evaluation acquisition module 302 may include: a factor acquisition module and an evaluation generation submodule, wherein:

[0115] The factor acquisition module is used to acquire the first performance evaluation factor and the second performance evaluation factor of each RPA robot in the RPA cluster. The first performance evaluation factor is associated with the first environment where the RPA robot is located, and the second performance evaluation factor is associated with the second environment where the RPA robot is located.

[0116] The evaluation generation submodule is used to generate performance evaluation information for each RPA robot based on the first performance evaluation factor and the second performance evaluation factor.

[0117] In this embodiment, a first performance evaluation factor and a second performance evaluation factor are obtained for each RPA robot. The first performance evaluation factor is associated with the first environment in which the RPA robot is located, and the second performance evaluation factor is associated with the second environment in which the RPA robot is located. The first performance evaluation factor and the second performance evaluation factor of each RPA robot constitute the performance evaluation information of each RPA robot, ensuring that a comprehensive performance evaluation of the RPA robot can be performed.

[0118] In some optional implementations of this embodiment, the selection module 303 may include: an evaluation value calculation submodule and a robot selection submodule, wherein:

[0119] The evaluation value calculation submodule is used to calculate the performance evaluation value of each RPA robot based on the performance evaluation information of each RPA robot.

[0120] The robot selection submodule is used to select at least one RPA robot from among the RPA robots based on the obtained performance evaluation values.

[0121] In this embodiment, based on the performance evaluation information of each RPA robot, the performance evaluation value of each RPA robot is calculated. The performance evaluation value represents the task processing capability of each RPA robot in terms of numerical value. Based on the performance evaluation value, at least one RPA robot with strong task processing capability is selected from all RPA robots to ensure the execution efficiency of the task to be processed.

[0122] In some optional implementations of this embodiment, the configuration generation module 304 may include: an information acquisition submodule and a configuration generation submodule, wherein:

[0123] The information acquisition submodule is used to acquire task evaluation information for tasks to be processed.

[0124] The configuration generation submodule is used to generate execution configuration information based on at least one selected RPA robot and task evaluation information. The execution configuration information includes the RPA working mode and execution order type.

[0125] In this embodiment, task evaluation information of the task to be processed is obtained, which evaluates and describes the task to be processed; based on the selected RPA robot and the task evaluation information, execution configuration information is generated, which includes the RPA working mode and execution order type, and instructs the execution of the task.

[0126] In some optional implementations of this embodiment, the task execution module 305 may include: a queue adjustment submodule and a task execution submodule, wherein:

[0127] The queue adjustment submodule is used to adjust the task execution queue of at least one RPA robot according to the execution order type in the execution configuration information, wherein the execution order type includes sequential execution and priority execution.

[0128] The task execution submodule is used to call at least one RPA robot based on the RPA working mode in the execution configuration information according to the adjusted task execution queue, so as to execute the task to be processed through at least one RPA robot and obtain the task execution result.

[0129] In this embodiment, the task execution queue of the RPA robot is adjusted according to the execution order type in the execution configuration information. Based on the adjusted task execution queue and the execution configuration information, the RPA robot is invoked to execute the tasks to be processed, thus ensuring the correct processing of the tasks to be processed.

[0130] In some optional implementations of this embodiment, the RPA robot control device 300 may further include: a status acquisition module, a first communication module, and a second communication module, wherein:

[0131] The status acquisition module is used to acquire the communication status of each RPA robot.

[0132] The first communication module is used to communicate with the RPA robot according to a preset first communication method when the RPA robot is in the first communication state. The first communication method is network communication.

[0133] The second communication module is used to communicate with the RPA robot according to a preset second communication method when the RPA robot is in the second communication state. The second communication method includes Bluetooth, infrared and LoRa.

[0134] In this embodiment, the communication status of the RPA robot is obtained, and different communication methods are used to communicate according to the communication status, ensuring that communication with the RPA robot can be carried out in various situations, and that the RPA robot can be controlled in various situations.

[0135] In some optional implementations of this embodiment, the RPA robot control device 300 may further include: a data acquisition module, a data cleaning module, and a data processing module, wherein:

[0136] The data acquisition module is used to acquire data to be processed, including the running data and logs of each RPA robot, as well as the task execution results of each task to be processed.

[0137] The data cleaning module is used to clean the data to be processed, resulting in cleaned data.

[0138] The data processing module is used to process the cleaned data according to a preset data processing strategy to obtain processed data. The data processing strategy is matched with the data type of the data to be processed.

[0139] In this embodiment, the data to be processed is acquired, including the running data and logs of each RPA robot, as well as the task execution results of each task to be processed; the data to be processed is cleaned to obtain cleaned data; the cleaned data is processed according to a preset data processing strategy to obtain processed data, thus completing the data fusion and aggregation processing in the RPA cluster.

[0140] To address the aforementioned technical problems, embodiments of this application also provide a computer device. Please refer to [link / reference needed]. Figure 4 , Figure 4 This is a basic structural block diagram of the computer device in this embodiment.

[0141] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected via a system bus. It should be noted that only the computer device 4 with components 41-43 is shown in the figure; however, it should be understood that it is not required to implement all the shown components, and more or fewer components can be implemented alternatively. Those skilled in the art will understand that the computer device described here is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0142] The computer device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device can interact with the user via a keyboard, mouse, remote control, touchpad, or voice control.

[0143] The memory 41 includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 41 may be an internal storage unit of the computer device 4, such as the hard disk or memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 4. Of course, the memory 41 may also include both the internal storage unit and its external storage device of the computer device 4. In this embodiment, the memory 41 is typically used to store the operating system and various application software installed on the computer device 4, such as computer-readable instructions for RPA robot control methods. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or will be output.

[0144] In some embodiments, the processor 42 may be a central processing unit (CPU), controller, microcontroller, microprocessor, or other data processing chip. The processor 42 is typically used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to execute computer-readable instructions stored in the memory 41 or to process data, for example, to execute computer-readable instructions of the RPA robot control method.

[0145] The network interface 43 may include a wireless network interface or a wired network interface, which is typically used to establish communication connections between the computer device 4 and other electronic devices.

[0146] The computer device provided in this embodiment can execute the above-described RPA robot control method. Here, the RPA robot control method can be any of the RPA robot control methods described in the various embodiments above.

[0147] In this embodiment, a task to be processed is received; performance evaluation information of each RPA robot in the RPA cluster is obtained. The performance evaluation information is used to evaluate the task processing capability of each RPA robot, so that at least one RPA robot with strong task processing capability can be selected based on the performance evaluation information, ensuring the processing efficiency of the task to be processed; execution configuration information is generated based on the selected at least one RPA robot and the task to be processed. The execution configuration information instructs the selected at least one RPA robot on how to process the task to be processed, further ensuring that the task to be processed can be executed reasonably, improving the efficiency and accuracy of the task execution results obtained after task execution, and realizing precise management and control of the RPA robots in the RPA cluster.

[0148] This application also provides another embodiment, namely, providing a computer-readable storage medium storing computer-readable instructions that can be executed by at least one processor to cause the at least one processor to perform the steps of the RPA robot control method described above.

[0149] In this embodiment, a task to be processed is received; performance evaluation information of each RPA robot in the RPA cluster is obtained. The performance evaluation information is used to evaluate the task processing capability of each RPA robot, so that at least one RPA robot with strong task processing capability can be selected based on the performance evaluation information, ensuring the processing efficiency of the task to be processed; execution configuration information is generated based on the selected at least one RPA robot and the task to be processed. The execution configuration information instructs the selected at least one RPA robot on how to process the task to be processed, further ensuring that the task to be processed can be executed reasonably, improving the efficiency and accuracy of the task execution results obtained after task execution, and realizing precise management and control of the RPA robots in the RPA cluster.

[0150] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0151] Obviously, the embodiments described above are only some embodiments of this application, not all embodiments. The accompanying drawings show preferred embodiments of this application, but do not limit the patent scope of this application. This application can be implemented in many different forms; rather, the purpose of providing these embodiments is to provide a more thorough and comprehensive understanding of the disclosure of this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this application's specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the scope of patent protection of this application.

Claims

1. An RPA robot control method, characterized by, Includes the following steps: Receive pending tasks; Obtain performance evaluation information for each RPA robot in the RPA cluster. The performance evaluation information includes multiple evaluation factors to evaluate the task processing performance of each RPA robot. Based on the performance evaluation information of each RPA robot, at least one RPA robot is selected from the RPA cluster; Execution configuration information is generated based on at least one selected RPA robot and the task to be processed; Based on the execution configuration information, at least one RPA robot is invoked to execute the task to be processed, and the task execution result is obtained; The step of obtaining the performance evaluation information of each RPA robot in the RPA cluster includes: Obtain the first performance evaluation factor and the second performance evaluation factor for each RPA robot in the RPA cluster. The first performance evaluation factor is associated with the first environment where the RPA robot is located, and the second performance evaluation factor is associated with the second environment where the RPA robot is located. The first environment refers to the virtual machine where the RPA robot is located, and the second environment refers to the terminal device where the RPA robot is located, or the terminal device where the virtual machine is located. Based on the first performance evaluation factor and the second performance evaluation factor of each RPA robot, the performance evaluation information of each RPA robot is generated respectively.

2. The RPA robot control method of claim 1, wherein, The step of selecting at least one RPA robot from the RPA cluster based on the performance evaluation information of each RPA robot includes: Calculate the performance evaluation value of each RPA robot based on the performance evaluation information of each RPA robot; At least one RPA robot is selected from the RPA robots based on the obtained performance evaluation values.

3. The RPA robot control method of claim 1, wherein, The step of generating execution configuration information based on at least one selected RPA robot and the task to be processed includes: Obtain the task evaluation information of the task to be processed; Based on at least one selected RPA robot and the task evaluation information, execution configuration information is generated, which includes the RPA working mode and execution order type.

4. The RPA robot control method of claim 3, wherein, The step of invoking at least one RPA robot to execute the task to be processed according to the execution configuration information and obtaining the task execution result includes: The task execution queue of the at least one RPA robot is adjusted according to the execution order type in the execution configuration information, wherein the execution order type includes sequential execution and priority execution; Based on the adjusted task execution queue, at least one RPA robot is invoked according to the RPA working mode in the execution configuration information to execute the task to be processed through the at least one RPA robot and obtain the task execution result.

5. The RPA robot control method of claim 1, wherein, The method further includes: For each RPA robot, obtain the communication status of the RPA robot; When the RPA robot is in the first communication state, it communicates with the RPA robot according to a preset first communication method, wherein the first communication method is network communication; When the RPA robot is in the second communication state, it communicates with the RPA robot according to a preset second communication method, which includes Bluetooth, infrared and LoRa.

6. The RPA robot control method of claim 1, wherein, The method further includes: Acquire the data to be processed, which includes the running data and running logs of each RPA robot, as well as the task execution results of each task to be processed; The data to be processed is cleaned to obtain cleaned data; The cleaned data is processed according to a preset data processing strategy to obtain processed data, wherein the data processing strategy is matched with the data type of the data to be processed.

7. An RPA robot control device characterized by, include: The task receiving module is used to receive tasks to be processed. The evaluation acquisition module is used to acquire performance evaluation information of each RPA robot in the RPA cluster. The performance evaluation information includes multiple evaluation factors to evaluate the task processing performance of each RPA robot. The selection module is used to select at least one RPA robot from the RPA cluster based on the performance evaluation information of each RPA robot; A configuration generation module is used to generate execution configuration information based on at least one selected RPA robot and the task to be processed; The task execution module is used to invoke at least one RPA robot to execute the task to be processed according to the execution configuration information, and obtain the task execution result; The evaluation acquisition module includes: a factor acquisition module and an evaluation generation submodule; The factor acquisition module is used to acquire a first performance evaluation factor and a second performance evaluation factor for each RPA robot in the RPA cluster. The first performance evaluation factor is associated with a first environment where the RPA robot is located, and the second performance evaluation factor is associated with a second environment where the RPA robot is located. The first environment refers to the virtual machine where the RPA robot is located, and the second environment refers to the terminal device where the RPA robot is located, or the terminal device where the virtual machine is located. The evaluation generation submodule is used to generate performance evaluation information for each RPA robot based on the first performance evaluation factor and the second performance evaluation factor of each RPA robot.

8. A computer device comprising a memory and a processor, the memory storing computer-readable instructions, wherein the processor, when executing the computer-readable instructions, implements the steps of the RPA robot control method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the RPA robot control method as described in any one of claims 1 to 6.

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