CN113835830A - AI-based RPA cluster management method, device and storage medium - Google Patents
AI-based RPA cluster management method, device and storage medium Download PDFInfo
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Abstract
The utility model provides a RPA cluster management method based on AI, which is applied to RPA cluster manager and comprises the following steps: receiving task information and parameter information related to an RPA task; determining a target basic mirror image snapshot according to the task information; and creating an RPA virtual instance based on the target basic mirror image snapshot, wherein the RPA virtual instance is used for acquiring and executing an RPA task according to the parameter information, and the RPA virtual instance can be managed through an RPA cluster manager, so that the operation and maintenance workload of an RPA robot is effectively reduced, the utilization rate of system resources is improved, and the enterprise operation cost is effectively reduced.
Description
Technical Field
The present disclosure relates to the field of image detection technologies, and in particular, to an AI-based RPA cluster management method, apparatus, and storage medium.
Background
Robot Process Automation (RPA) is a Process task that simulates human operations on a computer through specific robot software and automatically executes according to rules. With the wide application of RPA robots, more and more manpower work is taken over by RPA robots, and more RPA robots are put into production work.
Artificial Intelligence (AI) is a technical science that studies and develops theories, methods, techniques and application systems for simulating, extending and expanding human Intelligence. Natural Language Processing (NLP) is an important direction in the fields of computer science and artificial intelligence. It studies various theories and methods that enable efficient communication between humans and computers using natural language.
In the related art, each new RPA robot needs to go through a plurality of steps, including: opening a physical machine or a virtual machine, judging whether an operating system meets requirements (if the operating system does not meet the requirements, the operating system and corresponding software need to be reinstalled), installing RPA robot software, configuring the RPA robot software and connecting the RPA robot software with an RPA controller, testing whether the RPA robot can be normally used and is on-line operated. In addition, each physical machine or virtual machine system environment is different, and different configurations may need to be made for each machine, and the operation and maintenance workload is quite large. If the number of the RPA robots exceeds a certain number, special operation and maintenance personnel are needed for management, and the management cost is gradually increased. Meanwhile, because the operation and maintenance personnel perform manual operation, the operation and maintenance accidents are easily caused by the misoperation.
Disclosure of Invention
The application provides an RPA cluster management method, an RPA cluster management device and a storage medium based on AI, which aim to solve at least one of the technical problems in the related art to a certain extent.
An embodiment of a first aspect of the present application provides an AI-based RPA cluster management method, which is applied to an RPA cluster manager, and the method includes: receiving task information and parameter information related to an RPA task; determining a target basic mirror image snapshot according to the task information; and creating an RPA virtual instance based on the target base mirror image snapshot, wherein the RPA virtual instance is used for acquiring and executing an RPA task according to the parameter information.
In some embodiments, determining the target base mirror snapshot based on the task information includes: calling a Natural Language Processing (NLP) service to identify the task information and determining a task type corresponding to the task information; and determining a base mirror snapshot corresponding to the task type from the plurality of candidate base mirror snapshots as a target base mirror snapshot.
In some embodiments, the RPA virtual instance is a temporary RPA virtual instance, and the method further comprises: and releasing the resources of the temporary RPA virtual instance when the temporary RPA virtual instance finishes the RPA task.
In some embodiments, creating an RPA virtual instance based on the target base image snapshot includes: and based on the target basic mirror image snapshot, creating an RPA virtual instance through a preset program interface.
In some embodiments, further comprising: receiving mirror image modification information through a program interface; and according to the mirror image modification information, creating, deleting and modifying the plurality of candidate base mirror image snapshots.
In some embodiments, the RPA cluster manager is implemented based on at least one of cloud services, infrastructure as a service, container technology, and the RPA cluster manager is configured in a cluster form.
An embodiment of a second aspect of the present application provides an AI-based RPA cluster management apparatus, including: the first receiving module is used for receiving task information and parameter information related to the RPA task; the determining module is used for determining a target basic mirror image snapshot according to the task information; and the creating module is used for creating an RPA virtual instance based on the target basic mirror snapshot, wherein the RPA virtual instance is used for acquiring an RPA task according to the parameter information and executing the RPA task.
In some embodiments, the determining module comprises: the identification submodule is used for calling the natural language processing NLP service to identify the task information and determining the task type corresponding to the task information; and the determining submodule is used for determining a basic mirror snapshot corresponding to the task type from the plurality of candidate basic mirror snapshots to serve as a target basic mirror snapshot.
In some embodiments, the RPA virtual instance is a temporary RPA virtual instance, and the apparatus further comprises: and the releasing module is used for releasing the resources of the temporary RPA virtual instance when the temporary RPA virtual instance finishes the RPA task.
In some embodiments, the creating module is specifically configured to: and based on the target basic mirror image snapshot, creating an RPA virtual instance through a preset program interface.
In some embodiments, the apparatus further comprises: the second receiving module is used for receiving mirror image modification information through a program interface; and the modifying module is used for creating, deleting and modifying the candidate base mirror image snapshots according to the mirror image modifying information.
An embodiment of a third aspect of the present application provides an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the AI-based RPA cluster management method of the embodiments of the present application.
A fourth aspect of the present application provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the AI-based RPA cluster management method disclosed in the embodiments of the present application.
In this embodiment, the RPA cluster manager receives task information and parameter information related to an RPA task, determines a target base mirror snapshot according to the task information, and creates an RPA virtual instance based on the target base mirror snapshot, where the RPA virtual instance is used to acquire and execute an RPA task according to the parameter information, and can manage an RPA virtual instance (i.e., an RPA robot) through the RPA cluster manager, thereby reducing manual intervention, effectively reducing operation and maintenance workload of the RPA robot, and simultaneously improving utilization rate of system resources, thereby effectively reducing enterprise operation cost.
Additional aspects and advantages of the disclosure will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the disclosure.
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The foregoing and/or additional aspects and advantages of the present disclosure will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a schematic structural diagram of an RPA management system provided according to an embodiment of the present disclosure;
fig. 2 is a schematic flowchart of an AI-based RPA cluster management method according to an embodiment of the present disclosure;
fig. 3 is a schematic flowchart of an AI-based RPA cluster management method according to another embodiment of the present disclosure;
fig. 4 is a schematic flowchart of an AI-based RPA cluster management method according to an embodiment of the present disclosure;
fig. 5 is a schematic view of an application scenario of an RPA system provided according to an embodiment of the present disclosure;
fig. 6 is a schematic diagram of an AI-based RPA cluster management apparatus according to another embodiment of the present disclosure;
FIG. 7 illustrates a block diagram of an exemplary computer device suitable for use to implement embodiments of the present application.
Detailed Description
Reference will now be made in detail to the embodiments of the present disclosure, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the drawings are exemplary only for the purpose of illustrating the present disclosure and should not be construed as limiting the same. On the contrary, the embodiments of the disclosure include all changes, modifications and equivalents coming within the spirit and terms of the claims appended hereto.
In view of the technical problems mentioned in the background art that the operation and maintenance workload of the RPA robot in the related art is quite large and operation and maintenance accidents are easily caused, the technical solution of the present embodiment provides an AI-based RPA cluster management method, which is described below with reference to specific embodiments.
It should be noted that an execution main body of the AI-based RPA cluster management method according to this embodiment may be an AI-based RPA cluster management device, which may be implemented in a software and/or hardware manner, and the device may be configured in an electronic device, and the electronic device may include, but is not limited to, a terminal, a server, and the like.
Fig. 1 is a schematic structural diagram of an RPA management system provided according to an embodiment of the present disclosure, and as shown in fig. 1, the RPA management system may include an RPA controller, a robot cluster manager, and a plurality of RPA robots (RPA virtual instances).
The RPA controller is used for uniformly scheduling RPA tasks and distributing the tasks to the RPA robot; the robot cluster manager, which can also be called as RPA cluster manager, is connected with the RPA controller, and stores the basic mirror image snapshots of various RPA robots for creating and releasing the RPA robots; and the RPA robot is used for executing the tasks issued by the RPA controller and reporting the task running condition to the RPA controller.
In some embodiments, the RPA cluster manager may be implemented in a variety of ways, such as: cloud service realization based on cloud service providers (Ali cloud/Teng cloud/Hua cloud); or Infrastructure as a Service (Iaas) based implementations; or may be implemented based on container technology (e.g., k8s, swarm, etc.), and the RPA cluster manager may be configured in cluster form.
Fig. 2 is a schematic flowchart of an AI-based RPA cluster management method according to an embodiment of the present disclosure, where the AI-based RPA cluster management method is applied to an RPA cluster manager, and as shown in fig. 2, the AI-based RPA cluster management method includes:
s201: task information and parameter information related to the RPA task are received.
In the embodiment of the present disclosure, the RPA cluster manager first receives task information and parameter information related to an RPA task, for example: the RPA cluster manager receives the task information and the parameter information from the RPA controller.
The task information is used to describe information related to the RPA task, and includes, for example: the information of the RPA virtual instance (RPA robot) required to process the RPA task, the name of the RPA task, the execution time of the RPA task, and any other possible information, without limitation.
And the parameter information, which may also be referred to as a start parameter, is used for the RPA virtual instance to connect to the RPA controller.
Fig. 3 is a schematic diagram of an RPA task execution process provided according to an embodiment of the present disclosure, as shown in fig. 3, an RPA controller first creates an RPA task, and further, the RPA controller may invoke an RPA cluster manager to create an RPA virtual instance, where the invoking process may be represented as sending task information and parameter information to the RPA cluster manager. In this case, the RPA cluster manager may receive the task information and the parameter information.
S202: and determining the target basic mirror image snapshot according to the task information.
After receiving the task information and the parameter information, the RPA cluster manager further determines a target base mirror snapshot according to the task information.
The essence of the RPA robot is software, which can be realized by a base mirror snapshot, and the RPA cluster manager can store a plurality of base mirror snapshots in advance.
The base mirror snapshot determined according to the task information may be referred to as a target base mirror snapshot, that is, the target base mirror snapshot is related to the task information.
In some embodiments, the task information may include information of the target base mirror snapshot, and the RPA cluster manager may directly identify the information of the target base mirror snapshot from the task information, so as to determine the target base mirror snapshot. In addition, the target base image snapshot may be determined in any other possible manner, which is not limited in this respect.
S203: and creating an RPA virtual instance based on the target basic mirror snapshot, wherein the RPA virtual instance is used for acquiring and executing an RPA task according to the parameter information.
After determining the target base image snapshot, the RPA cluster manager may create an RPA virtual instance based on the target base image snapshot, that is: an RPA robot is created. As shown in fig. 3, after the RPA cluster manager creates the RPA virtual instance, it starts the RPA virtual instance. The RPA virtual instance is initialized firstly, then is connected with the RPA controller according to the parameter information, acquires the RPA task from the RPA controller, further executes the RPA task, and reports the task state of the RPA task to the RPA controller.
In some embodiments, the RPA cluster manager may further provide an API program interface operable to create the RPA virtual instance through a preset API program interface in the process of creating the RPA virtual instance based on the target base image snapshot.
In other embodiments, the RPA cluster manager may further receive image modification information through the API program interface, where the image modification information is used to modify the plurality of candidate base image snapshots stored by the RPA cluster manager.
Further, according to the image modification information, creating, deleting and modifying operations are carried out on the candidate base image snapshots. Therefore, the candidate basic mirror image snapshot of the RPA cluster manager can be updated in real time, and the processing requirement of the RPA task is met.
In this embodiment, the RPA cluster manager receives task information and parameter information related to an RPA task, determines a target base mirror snapshot according to the task information, and creates an RPA virtual instance based on the target base mirror snapshot, where the RPA virtual instance is used to acquire and execute an RPA task according to the parameter information, and can manage an RPA virtual instance (i.e., an RPA robot) through the RPA cluster manager, thereby reducing manual intervention, effectively reducing operation and maintenance workload of the RPA robot, and simultaneously improving utilization rate of system resources, thereby effectively reducing enterprise operation cost.
Fig. 4 is a schematic flowchart of an AI-based RPA cluster management method according to another embodiment of the present disclosure, and as shown in fig. 4, the method includes:
s401: task information and parameter information related to the RPA task are received.
For a specific description of S401, refer to the above embodiments, which are not described herein.
S402: and calling a Natural Language Processing (NLP) service to identify the task information and determining a task type corresponding to the task information.
In the operation of determining the target base mirror image snapshot, the RPA cluster manager may first invoke a Natural Language Processing (NLP) service to identify the task information and determine a task type corresponding to the task information.
The RPA task may have a corresponding task type, for example: financial type, monitoring type, and any other possible type, without limitation.
The RPA cluster manager can preset Natural Language Processing (NLP) service, and the NLP service can identify the task information and determine the task type.
The natural language processing NLP service may be, for example, a natural language processing model with which task information may be identified, such as: and identifying the task name in the task information so as to determine the task type corresponding to the task information.
It is understood that the above example is only illustrative of a natural language processing model, and in practical applications, any other possible artificial intelligence model may be used to identify any task information and determine the task type, which is not limited herein.
S403: and determining a base mirror snapshot corresponding to the task type from the plurality of candidate base mirror snapshots as a target base mirror snapshot.
The base mirror snapshots of multiple RPA robots pre-stored in the RPA cluster manager may be referred to as candidate base mirror snapshots, and different candidate base mirror snapshots may correspond to different task types, that is, different RPA virtual instances may be created through different candidate base mirror snapshots to execute RPA tasks of a task type.
In the embodiment of the disclosure, for RPA tasks of different task types, corresponding target base mirror image snapshots can be selected, and then RPA virtual instances of corresponding types can be created to execute the RPA tasks. Therefore, the RPA task can correspond to the RPA virtual instance, the RPA task can be accurately executed, and the execution efficiency and effect of the task are improved.
S404: and creating an RPA virtual instance based on the target basic mirror snapshot, wherein the RPA virtual instance is used for acquiring and executing an RPA task according to the parameter information.
For a specific description of S404, refer to the above embodiments, which are not described herein.
S405: and releasing the resources of the temporary RPA virtual instance when the temporary RPA virtual instance finishes the RPA task.
In some embodiments, the RPA cluster manager may create the temporary RPA virtual instance and the fixed RPA virtual instance through an API program interface. As shown in fig. 3, when the temporary RPA virtual instance completes the RPA task, the RPA cluster manager may release the resource of the temporary RPA virtual instance, so as to reduce the resource occupation of the system.
In this embodiment, the RPA cluster manager receives task information and parameter information related to an RPA task, determines a target base mirror snapshot according to the task information, and creates an RPA virtual instance based on the target base mirror snapshot, where the RPA virtual instance is used to acquire and execute an RPA task according to the parameter information, and can manage an RPA virtual instance (i.e., an RPA robot) through the RPA cluster manager, thereby reducing manual intervention, effectively reducing operation and maintenance workload of the RPA robot, and simultaneously improving utilization rate of system resources, thereby effectively reducing enterprise operation cost. In addition, corresponding target base mirror image snapshots can be selected for the RPA tasks of different task types, and then RPA virtual instances of corresponding types can be created to execute the RPA tasks. Therefore, the RPA task can correspond to the RPA virtual instance, the RPA task can be accurately executed, and the execution efficiency and effect of the task are improved. And the RPA cluster manager can release the resources of the temporary RPA virtual instance, thereby reducing the resource occupation of the system.
In a specific example, a certain group of companies have quite a lot of factories and complex production links, so that the financial departments of all factories are insufficient in labor, all production financial data can be reported after 5 o ' clock every day, financial personnel need to overtake to 10 o ' clock or even 12 o ' clock every day to collect the financial data of the day into a report, the company prepares 100 virtual machines by using a set of traditional RPA system, and each virtual machine is provided with the same RPA robot software. The daily operation and maintenance workload is quite large, and the operation and maintenance department shifts to 10 or even 12 hours each day. In this case, the technical solution provided in this embodiment may be applied to a company, for example, fig. 5 is an application scenario schematic diagram of an RPA system provided according to an embodiment of the present disclosure, and as shown in fig. 5, a robot cluster manager based on k8s may create multiple RPA virtual instances, for example: temporary robot instance 1, temporary robot instance 2.. temporary robot instance n, respectively execute report summarization tasks (i.e., RPA tasks) of a plurality of factories through a plurality of RPA virtual instances.
The workflow related to the operation and maintenance personnel is as follows:
1. deploying RPA controller (Single-point or Cluster)
2. Deployment k8s Cluster (Cluster mode)
3. Configuring connection of RPA controller and robot cluster manager
4. Uploading mirror images for RPA robot
5. If there is a fixed RPA robot, then a mirror image is selected and a fixed RPA robot instance is created.
The robot instance is managed in a k8s cluster mode, system resources occupied by a small RPA process are limited, and the overall resource utilization rate is improved to over 90% from the original 10% to 20%. Moreover, the hardware resources used by the cluster size are reduced from the original 100 virtual machines to 20 virtual machines. The waste of hardware resources is greatly reduced, and the use efficiency of the system is improved.
Fig. 6 is a schematic diagram of an AI-based RPA cluster management apparatus according to another embodiment of the present disclosure. As shown in fig. 6, the AI-based RPA cluster management apparatus 60 includes:
a first receiving module 601, configured to receive task information and parameter information related to an RPA task;
a determining module 602, configured to determine a target base mirror snapshot according to task information; and
the creating module 603 is configured to create an RPA virtual instance based on the target base image snapshot, where the RPA virtual instance is configured to obtain an RPA task according to the parameter information and execute the RPA task.
In some embodiments, the determining module 602 includes: the identification submodule is used for calling the natural language processing NLP service to identify the task information and determining the task type corresponding to the task information; and the determining submodule is used for determining a basic mirror snapshot corresponding to the task type from the plurality of candidate basic mirror snapshots to serve as a target basic mirror snapshot.
In some embodiments, the RPA virtual instance is a temporary RPA virtual instance, and the apparatus 60 further comprises: and the releasing module is used for releasing the resources of the temporary RPA virtual instance when the temporary RPA virtual instance finishes the RPA task.
In some embodiments, the creating module 603 is specifically configured to: and based on the target basic mirror image snapshot, creating an RPA virtual instance through a preset program interface.
In some embodiments, the apparatus 60 further comprises: the second receiving module is used for receiving mirror image modification information through a program interface; and the modifying module is used for creating, deleting and modifying the candidate base mirror image snapshots according to the mirror image modifying information.
In some embodiments, the apparatus 60 is implemented based on at least one of cloud services, infrastructure as a service, container technology, and the RPA cluster manager may be configured in a cluster form.
The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.
In order to implement the foregoing embodiments, the present application further proposes a computer program product, which when executed by an instruction processor in the computer program product, executes the AI-based RPA cluster management method as proposed in the foregoing embodiments of the present application.
FIG. 7 illustrates a block diagram of an exemplary computer device suitable for use to implement embodiments of the present application. The computer device 12 shown in fig. 7 is only an example, and should not bring any limitation to the function and the scope of use of the embodiments of the present application.
As shown in FIG. 7, computer device 12 is in the form of a general purpose computing device. The components of computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.
Although not shown in FIG. 7, a disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a Compact disk Read Only Memory (CD-ROM), a Digital versatile disk Read Only Memory (DVD-ROM), or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the application.
A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally perform the functions and/or methodologies of the embodiments described herein.
The processing unit 16 executes various functional applications and RPA cluster management by running a program stored in the system memory 28, for example, implementing the AI-based RPA cluster management method mentioned in the foregoing embodiments.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.
It should be noted that, in the description of the present application, the terms "first", "second", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. In addition, in the description of the present application, "a plurality" means two or more unless otherwise specified.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and the scope of the preferred embodiments of the present application includes other implementations in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.
Claims (12)
1. An AI-based RPA cluster management method, applied to an RPA cluster manager, the method comprising:
receiving task information and parameter information related to an RPA task;
determining a target basic mirror image snapshot according to the task information; and
and creating an RPA virtual instance based on the target basic mirror image snapshot, wherein the RPA virtual instance is used for acquiring and executing the RPA task according to the parameter information.
2. The method of claim 1, wherein determining a target base mirror snapshot based on the task information comprises:
calling a Natural Language Processing (NLP) service to identify the task information and determining a task type corresponding to the task information; and
and determining a base mirror snapshot corresponding to the task type from a plurality of candidate base mirror snapshots as the target base mirror snapshot.
3. The method of claim 1, wherein the RPA virtual instance is a temporary RPA virtual instance, and the method further comprises:
and releasing the resources of the temporary RPA virtual instance when the temporary RPA virtual instance finishes the RPA task.
4. The method of claim 2, wherein creating an RPA virtual instance based on the target base mirror snapshot comprises:
and based on the target basic mirror image snapshot, creating an RPA virtual instance through a preset program interface.
5. The method of claim 4, further comprising:
receiving mirror image modification information through the program interface; and
and according to the mirror image modification information, creating, deleting and modifying the candidate base mirror image snapshots.
6. An AI-based RPA cluster management apparatus, comprising:
the first receiving module is used for receiving task information and parameter information related to the RPA task;
the determining module is used for determining a target basic mirror image snapshot according to the task information; and
and the creating module is used for creating an RPA virtual instance based on the target basic mirror snapshot, wherein the RPA virtual instance is used for acquiring the RPA task according to the parameter information and executing the RPA task.
7. The apparatus of claim 6, wherein the determining module comprises:
the identification submodule is used for calling a Natural Language Processing (NLP) service to identify the task information and determining a task type corresponding to the task information; and
and the determining submodule is used for determining a basic mirror snapshot corresponding to the task type from a plurality of candidate basic mirror snapshots to serve as the target basic mirror snapshot.
8. The apparatus of claim 6, wherein the RPA virtual instance is a temporary RPA virtual instance, and the apparatus further comprises:
and the releasing module is used for releasing the resources of the temporary RPA virtual instance when the temporary RPA virtual instance finishes the RPA task.
9. The apparatus of claim 7, wherein the creation module is specifically configured to: and based on the target basic mirror image snapshot, creating an RPA virtual instance through a preset program interface.
10. The apparatus of claim 9, wherein the apparatus further comprises:
the second receiving module is used for receiving mirror image modification information through the program interface; and
and the modifying module is used for creating, deleting and modifying the candidate base mirror image snapshots according to the mirror image modifying information.
11. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-5.
12. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-5.
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